System and method for dynamic stereoscopic calibration

Information

  • Patent Grant
  • 11315276
  • Patent Number
    11,315,276
  • Date Filed
    Friday, March 6, 2020
    4 years ago
  • Date Issued
    Tuesday, April 26, 2022
    2 years ago
  • CPC
    • G06T7/593
    • G06T7/85
    • H04N13/246
  • Field of Search
    • US
    • 348 047000
    • CPC
    • G06T7/593
    • G06T7/85
    • H04N13/246
  • International Classifications
    • G06T7/593
    • G06T7/80
    • H04N13/246
Abstract
Methods for stereo calibration of a dual-camera that includes a first camera and a second camera and system for performing such methods. In some embodiments, a method comprises obtaining optimized extrinsic and intrinsic parameters using initial intrinsic parameters and, optionally, initial extrinsic parameters of the cameras, estimating an infinity offset e using the optimized extrinsic and extrinsic parameters, and estimating a scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset parameter e, wherein the optimized extrinsic and extrinsic parameters, infinity offset e and scaling factor s are used together to provide stereo calibration that leads to improved depth estimation.
Description
FIELD

Embodiments disclosed herein relate in general to optical instrument calibration such as in stereoscopic digital cameras, and more particularly to stereoscopic calibration in dual-aperture digital cameras (“dual-cameras”) that are configured to be incorporated in vehicles as part of a driver assistance system.


BACKGROUND
Definitions

“Dynamic stereoscopic calibration”—estimation of “stereo parameters” of a stereo (dual) camera without a known calibration chart, while the stereo camera is in use, with or without it being moved.


“Stereo parameters”: stereo camera parameters that are required to be calibrated in order to produce a high precision depth map, comprising intrinsic parameters (for each camera) and extrinsic parameters (for each camera pair).


“Intrinsic parameters”: parameters that include focal length, optical axis in X and Y axes and lens distortion coefficients.


“Extrinsic parameters”: parameters that include three relative (between two cameras) angles (Yaw, Pitch and Roll) and three offsets (Tx, Ty and Tz).


“Disparity axis”: the axis of disparity (in our XYZ coordinate system, X if cameras are placed horizontally).


“Non-disparity axis”: the axis perpendicular to the disparity axis (in our XYZ coordinate system, Y if cameras are placed horizontally).


Advanced Driver-Assistance Systems (ADASs) are known. An ADAS included in vehicles combine sensors and algorithms to understand the vehicle's environment so that a driver of the vehicle can receive assistance or be warned of hazards. ADASs rely on computer vision, which plays a pivotal role in acquiring, processing, analyzing, and understanding the environment and surrounding objects. Often, ADASs use multi-camera systems with two or more cameras or “camera modules”.



FIG. 1A shows top views of vehicles having multi-camera systems in various arrangements. The term “vehicle” may apply to any vehicle including (but not limited) to a car, a motorcycle, a truck, a bus, an airplane, a bicycle, etc. In (a) a dual-camera system includes two cameras 102 and 104 arranged close to each other along the X axis. Here, X the disparity axis is X and the a non-disparity axis is Y. The two cameras may share a common housing. In (b) two cameras 106 and 108 are placed along the Y axis at a greater distance from each other than in (a). In (c), two cameras 110 and 112 are arranged along the X axis as disparity axis. In (d) there are four cameras 114, 116, 118 and 120, in (e) there are three cameras 122, 124 and 126 and in (f) there are two cameras 128 and 130, arranged as shown. The cameras are not limited to a particular type of camera. In an example, the cameras may be identical. In an example, cameras may differ in one or more of the following parameters: focal length, sensor size, pixel size and/or pitch, and/or f-number (f/#). In an example, a camera may be a color camera, a black and white camera or an infrared (IR) sensitive camera. In an example, a multi-camera system may additionally include infrared projectors, fiber optics, lasers, sensors, or a combination thereof (not shown).


Accurate depth maps of the environment are necessary for the computer vision to operate properly. A depth map is an image or image channel that contains information relating to the distance of surfaces of scene objects from a viewpoint.


A common solution for creating depth maps of the environment is using a stereoscopic camera or a dual-camera (a camera comprised of two sub-cameras) for imaging and estimating the distance of objects from the camera. Using a dual-camera for depth map creation depends on calculating disparity of the pixels of various objects in the field of view (FOV). In order to accurately translate disparity values in pixels to real-world depth in meters, there is a need for accurate camera stereo calibration.


Calibration of a stereo (or dual) camera system includes analyzing acquired data to assess the accuracy of the intrinsic and extrinsic parameters and adjusting accordingly.


Assuming all intrinsic and extrinsic parameters are known, an object's distance (or “depth) Z within the FOV of a vehicle dual-camera system can be calculated and/or estimated using equation 1:









Z
=


f
*
B


D
*
ps






(
1
)








where f is focal length, B is baseline, D is disparity in pixels, and “ps” is pixel size.


However, in practice, factory and dynamic calibration procedures suffer from estimation errors in intrinsic and/or extrinsic parameters that can be expressed in a revised equation 1′:









Z
=


s
*
f
*
B



(

D
+
e

)

*
ps






(

1


)








where “s” is an unknown scaling factor, an accumulative error in focal length estimation and translation along a disparity axis (i.e. Tx), and “e” represents a “infinity disparity error” or “infinity offset” that encapsulates the estimation error in optical axis location for both left (“L) and right (“R”) cameras (intrinsic parameters) as well as estimation errors in the rotation along the “non-disparity axis”, extrinsic parameters).



FIG. 1B shows an example of the possible discrepancies in depth estimation for two exemplary errors in e. Assume e is estimated with 0.5 pixels error or 1 pixels error. The figure shows a graph for a stereo system with f=6 mm, ps=0.0042 mm and B=120 mm (see Eq. 1′). The graph depicts diverging error percentages based on distance comparing a 0.5-pixel error to a 1-pixel error (“e” in equation 1′). The effect of even half a pixel error on the depth estimation is dramatic especially in high distances.


Manual stereo calibrations before installation of stereo or dual-cameras in a host vehicle are difficult. Maintaining a pre-installation stereo calibration is difficult, due to changes during camera lifecycle. Such changes may include (but are not limited to) heat expansions, vibrations and mechanical hits, which cause some of the calibration parameters to change over time. Calibrating a stereo (or dual) camera mounted behind a windshield is further complicated since the windshield may affect some of the calibration parameters of the stereo camera, e.g. by distorting the perspective or viewing angles of the camera. Therefore, the calibration may be performed only after installing the cameras in the host vehicle.


There have been a number of attempts to solve the stereoscopic camera calibration issue, however, none have been able to devise a solution that meets the needs of industry. Some of these solutions attempt to run structure from motion (SFM) algorithms. SFM uses complicated algorithms that track moving features in successive images to determine its structural information, then the image frames are processed to compute a depth map. This solution fails to meet the needs of industry because running these processes is inordinately difficult and computationally demanding for the cameras mounted in a moving car.


There is therefore a need for, and it would be advantageous to have dynamic stereoscopic calibration systems and methods that overcome the deficiencies in existing systems and methods that use SFM technology.


SUMMARY

In various embodiments, there are provided methods for dynamic stereoscopic calibration of a stereo digital camera including a first camera and a second camera, each camera having intrinsic parameters and extrinsic parameters, the method comprising: obtaining optimized extrinsic and intrinsic parameters based on input intrinsic parameters, and, optionally, input extrinsic parameters; estimating an offset parameter e using the optimized extrinsic and extrinsic parameters; estimating a scaling factor s using the optimized extrinsic and extrinsic parameters and estimated offset parameter e; and using the optimized extrinsic and extrinsic parameters, infinity offset e and scaling factor s to provide stereo calibration that leads to improved depth estimation.


In certain embodiments, a method for dynamic stereoscopic calibration disclosed herein may include selecting initial values for the intrinsic and/or extrinsic parameters of the first camera and initial values for the intrinsic parameters of the second camera. The initial values may be derived for example from the design of the camera (“nominal” values), from factory settings if calibration for each camera was done or from previous usage of the camera, etc. The calibration of the intrinsic and/or extrinsic parameters may include capturing at least one image from the first camera and at least one image from the second camera, matching corresponding points on the at least one image from the first camera to corresponding points on the at least one image from the second camera, and calculating optimized intrinsic and extrinsic parameters of the first camera and the second camera using epipolar geometry. This provides an initial calibration of the first camera and of the second camera with aligned epipolar lines. The various selections, calculations, processes etc. may be performed using a processor, and data/results of the processing may be stored in a memory.


Further actions to estimate offset parameter e and scaling factor s may include obtaining, at least two image pairs based upon images received from the first camera and the second camera, wherein the at least two image pairs are images sequentially taken via the first camera and the second camera, and wherein each pair of images (one from each camera) needs to be taken simultaneously; matching corresponding points on the at least two image pairs; and generating a disparity map, wherein the disparity map includes pixels matched from the corresponding points on the at least two image pairs, wherein pixels with constant disparity are identified as pixels at infinity distance.


In certain embodiments, the method includes storing the at least two image pairs in a memory.


In certain embodiments, the number of at least two image pairs captured from the first camera and the at least two image pairs captured from the second camera is determined by a processor.


In certain embodiments, the processor stops receiving at least two image pairs from the first camera and the second camera once a full FOV is captured.


In certain embodiments, the stereo digital camera is installed in a vehicle.


In certain embodiments, the stereo digital camera is configured to be incorporated in a vehicle as part of a driver assistance system.


In certain embodiments, the step of setting the initial intrinsic parameters of the first camera and the initial intrinsic parameters of the second camera includes a processor performing an initial guess for the intrinsic parameters for said stereo digital camera.


In certain embodiments, the step of selecting initial intrinsic parameters include factory calibration.


In certain embodiments, the selecting initial intrinsic parameters includes independent estimation from bundle adjustment.


In certain embodiments, selecting initial intrinsic parameters includes independent estimation from structure from motion (SFM).


In certain embodiments, the at least one image from the first camera and at least one image from the second camera are stored in memory.


In certain embodiments, the corresponding points on the at least one image from the first camera and the second camera are stored in memory.


In certain embodiments, the disparity map is stored in memory.


In certain embodiments, the steps for calibrating external and internal parameters are repeated to obtain a full FOV.


In certain embodiments, the steps for calibrating depth are repeated to obtain a full FOV.


In certain embodiments, the intrinsic parameters are selected from a group consisting of focal length, image distortion and optical axis.


In certain embodiments, the extrinsic parameters describe the translation and rotation of the one camera relative to the other.


In certain embodiments, the method includes using infinity disparity to compensate for estimation errors.


In certain embodiments, the method includes identifying moving objects in the at least two image pairs.


In certain embodiments, the method includes removing said moving objects from the disparity map.


In certain embodiments, the moving objects are identified using computer vision.


In certain embodiments, the moving objects are identified using high disparity values.


In certain embodiments, the method includes repeating the steps of the above referenced steps multiple times and averaging the results.


In an embodiment there is provided a method for dynamic stereo camera calibration, comprising obtaining at least two image pairs from a dual-camera, performing local registration of the at least two image pairs and obtaining a registration map, finding a minimal disparity in the registration map, calculating a minimum disparity value, defining a global minimal disparity value, and calibrating the dual-camera using the global minimal disparity value.





BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the application. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the application may be practiced. In the drawings:



FIG. 1A shows top views of vehicles having multi-camera systems in various arrangements;



FIG. 1B shows an example of possible discrepancies in depth estimation due to infinity disparity errors;



FIG. 2A shows a flowchart describing an exemplary embodiment for calibrating stereo parameters;



FIG. 2B shows a flowchart describing an exemplary embodiment for calibrating stereo parameters;



FIG. 3A shows a flowchart describing an exemplary embodiment for infinity correction in a method disclosed herein;



FIG. 3B shows a flowchart describing an exemplary embodiment for infinity correction in a method disclosed herein;



FIG. 4A shows a flowchart describing an exemplary embodiment of a method for estimating object scale in a method disclosed herein;



FIG. 4B shows a flowchart describing another exemplary embodiment of a method for estimating object scale in a method disclosed herein;



FIG. 5 describes a system installed in a vehicle and used for performing a method disclosed herein.





DETAILED DESCRIPTION

Embodiments disclosed herein describe methods of dynamic stereo calibration of the intrinsic and extrinsic parameters that include estimation of the “additional” parameters of equation 1′, scaling factor s and disparity error e. While all the parameters in the equation may be estimated in a factory (where the dual-camera is assembled and/or where the vehicle is assembled), the parameters may change during the life cycle of the camera due to a number of factors, including (but not limited to) shift between sub-cameras, tilt between sub-cameras, shift in each sub-camera between its lens and the sensor, change in the camera focal length, etc.


The present application efficiently calibrates the camera parameters to ensure that the camera is viewing its surroundings properly and is able to effectively calculate distance.



FIG. 2A shows a flowchart describing an embodiment of an exemplary method for calibrating a stereo camera's intrinsic and extrinsic parameters. In step 202, initial extrinsic and/or intrinsic parameters are selected for both cameras. An initial calibration parameter can be estimated from factory settings (all the nominal values known for the stereo camera—focal length, lens distortion, optical axis, base line, etc.), through independent estimation from bundle adjustment, structure from motion, previous calibration parameters, etc. Intrinsic and (optionally) extrinsic parameters are calibrated in step 204. The calibration includes obtaining a first set of R-L images in sub-step 208 and matching corresponding points in sub-step 210, using a feature extraction method on L and R images separately and finding the corresponding feature of L image in the right image. A minimum of four pairs of points are needed, but normally a few hundred pairs of points are used. The calibration continues with calculating extrinsic parameters in sub-step 212, using for example, Essential Metrix estimation and decomposition, estimating the 3 angles (Yaw, Pitch, Roll) and the translation up to unknown scale (three offsets) Tx, Ty, Tz. The intrinsic parameters are then refined in sub-step 214 using an optimization technique (i.e. gradient standard) to minimize the non-disparity axis error over selected intrinsic parameters. The refinement includes calculating the difference in image location for each matched feature point along the non-disparity axis (NDA). The goal of the optimization is to minimize the sum of absolute NDA over all matched features from all the images. In a perfectly calibrated stereo system, the sum of absolute NDA will converge to zero. For practical cases and for example, one can set a stop condition that the minimized average absolute NDA be within a “delta” value from zero. For example, the delta value may be 0.1 pixels. Using another stop condition and example, the stop condition may be a maximum absolute NDA smaller than 0.25 pixels.


The output of step 204 is optimized stereo calibration (i.e. intrinsic and extrinsic) parameters 206, i.e. a calibrated dual-camera output that allows rectifying the camera system's output into a pair of images having parallel epipolar lines. The optimized stereo calibration parameters are then used in estimating infinity offset e and scaling factor s.


This optimization problem can be solved with a number of optimization techniques such as gradient decent. Intrinsic parameters refined in this sub-step include focal length ratio between left and right cameras, lens distortion coefficients and “non-disparity” optical axis differences.



FIG. 2B shows a flowchart describing another embodiment of an exemplary method for calibrating a stereo camera's intrinsic and extrinsic parameters. This embodiment is similar to that of FIG. 2A, with the following changes:


1. Iterate steps 208 and 210 until sufficient matched points are gathered: this is an iterative process performed in step 211 until the matched features are evenly spread across the camera's FOV and distance from the camera, for example by having 5 corresponding points in a 3D box of N0×N0×FOV_Pdisp (N˜1/20 FOV P˜1/10 disparity range measured by pixels).


2. Iterate steps 212 and 214 until a stable state is reached: this is an iterative process performed in step 213. After intrinsic parameters were refined in step 214 recalculate extrinsic parameters in step 212 and refine until steady state is reached either in the parameter value or in the sum of absolute NDA.



FIG. 3A shows a flowchart describing an exemplary embodiment of a method for infinity correction (i.e. for estimating infinity offset e in equation 1′). The method is implemented using a dual-camera in a dynamic environment (e.g. while driving in a given vehicle). At least two sets of stereo images (i.e. four images, 2L and 2R) are obtained while in motion in step 302. Corresponding points in each set of L and R images are matched in step 304. Corresponding points between images in the two sets are matched in step 306. In contrast with the matching of left vs. right (L-R) features performed with a single set of images, in step 306 the match is left vs. left (L-L) and/or right vs. right (R-R) in various car positions (i.e. of a same point in different sets of stereo images obtained in step 304). A disparity map of corresponding points is generated in step 308. The generation of the disparity map includes calculating the disparity value in the two time frames for all features matched in both 304 and 306. This step must be done on rectified points, either by rectifying the input images (before step 302) or just rectifying the corresponding feature (before step 308). Rectification parameters (i.e. stereo parameters) are then obtained (estimated) in output 206. In certain embodiments, when “disparity” is mentioned, it is assumed either rectified images or rectified image coordinates are used. In step 310 pixels with constant disparity over different time steps (while the vehicle was in motion) are labeled as “infinity” distance. The infinity offset e, defined as the disparity of points at infinity is then estimated in step 312. In certain embodiments, this is done by averaging the disparity of all infinity labeled pixels. In certain embodiments, just one infinity labeled pixel is enough, although in practice a few dozen will be used.


Optionally, a step 314 of filtering stationary objects may be performed before estimating infinity offset e step 312. Objects that are stationary to the dual-camera and/or the given vehicle (e.g., another vehicle moving with the same velocity and in the same direction as the given vehicle) will have constant disparity (same as infinity pixels) and therefore should be filtered from the infinity offset estimation. The filtering may include for example thresholding pixels with large enough disparity (infinite disparity will be close to zero) or detecting cars/motorcycle/bikes by machine learning.



FIG. 3B shows a flowchart describing an exemplary embodiment of another method for infinity correction. This embodiments is similar to the one in FIG. 3A, except for an added loop (iteration) step 316, which iterates steps 302 to 308 to ensure that estimating infinity offset step 312 has a sufficient number of infinity pixels (in general more infinity labeled pixels are desired). i.e. reaches a steady state of infinity offset estimation.



FIG. 4A shows a flowchart describing an exemplary embodiment of a method for estimating scale (estimating scaling factor s in equation 1′). As in the estimation of e, the method is implemented using a dual-camera in a dynamic environment (e.g. while driving in a given vehicle). At least one set of stereo images is obtained in step 402 while in motion. Objects of known dimensions (OKDs) are detected in step 404 using a detection algorithms, by finding an OKD in one of the acquired images. We define a “detected OKD” as XOKD XOKD may be for example a license plate length, a traffic speed sign diameter, or any other objects that are identical to each other and/or have constant dimensions in a given place (city, state, country, continent, etc.). The corresponding points of each XOKD are matched in the corresponding stereo image in step 406 and the size of known objects in pixels is calculated in step 408. The size calculation may include using a segmentation algorithm to find all pixels associated with the object and to calculate its dimensions POKD (e.g. license plate length or traffic speed sign diameter). The disparity of the known dimension object is calculated in step 410 using (as in step 308) rectified images or rectified image coordinates. The distance of the XOKD from the dual-camera is calculated in step 412 using for example camera focal length and object pixel size as Distance=focal_length*XOKD/POKD. Scaling factor s is then estimated in step 414 using equation 1′ and the value of e from step 312.


In some embodiments, one set of images is needed since object dimensions may be known. In other embodiments, many sets of images can be obtained, preferably a thousand image sets, however fewer can be utilized effectively as well. A plurality of output estimations for s may be averages over many measurements.



FIG. 4B shows a flowchart describing another exemplary embodiment of a method for estimating scaling factor s. At least two sets of stereo (L and R) images are obtained while in motion in step 420. Stationary objects relative to the ground are found (detected) (e.g. by a detection algorithm for traffic sign/traffic lights, buildings, and/or cross-roads) in step 422. Corresponding points in each set are matched in step 424 in a manner similar to that in step 304. Corresponding points between images in the two sets are matched in step 426 in a manner similar to that in step 306. A disparity map of corresponding points is generated in step 428 in a manner similar to that in step 308. A distance AZ driven by the vehicle between the taking of each pair of sets of images is obtained (measured) in step 430, e.g. using the vehicle's velocity meter/GPS/external inertial measurement unit. The disparity of the stationary objects disparity and the driven distance are then used to estimate scaling factor s in step 432, using equation 1′ and equation 2 below (after e has been estimated in step 312).










Δ





Z

=



Z

i
+
1


-

Z
i


=



s
*
f
*
B

ps



(


1


D

i
+
1


+
e


-

1


D
i

+
e



)







(
2
)








Furthermore, s can be easily extracted and averaged across many samples.


In an alternate embodiment, a dual-camera system obtains a set of two images from the dual-camera. The system performs local registration of the set of two images and obtains a registration map. The system proceeds by finding the minimal disparity in the registration map, calculating the minimum of minimum disparity value, defining a global minimum disparity value, and calibrating the dual-camera using the global minimum disparity.


Image registration is the process of transforming different sets of data into one coordinate system. The data may be multiple photographs, data from different sensors, times, depths, or viewpoints.



FIG. 5 shows schematically an embodiment of an electronic device numbered 500 including a dual-aperture camera (as a particular example of a multi-aperture camera that can have more than two camera modules). Electronic device 500 comprises a first camera module 502 that includes a first lens module 504 that forms a first image recorded by a first image sensor 506 and a second camera module 510 that includes a second lens module 512 that forms an image recorded by a second image sensor 514. The two camera modules may be identical on different. For example, the two cameras may have similar or different FOVs. The cameras may be of different type, for example having image sensors sensitive to the visible (VIS) wavelength range or to the infrared (IR) or other wavelength range, time of flight (TOF) cameras, etc. Electronic device 500 may further comprise a processing unit or application processor (AP) 520. In some embodiments, initial or previous calibration data may be stored in memory 524 of the electronic device 500.


In use, a processing unit such as AP 520 may receive respective first and second image data (or 1st and 2nd images) from camera modules 502 and 510 and may supply camera control signals to camera modules 502 and 510 to ensure that both images are acquired simultaneously. After receiving at least one image from each camera, AP 520 will execute the processes described in FIGS. 2A, 2B, 3A, 3B and 4A, 4B. The final outcome will be updated stereo calibration parameters that may be stored in the memory unit 524, for further use.


It should be understood that where the claims or specification refer to “a” or “an” element, such reference is not to be construed as there being only one of that element.


Methods described herein can be implemented to calibrate camera parameters as often as every time a user turns on a car or multiple times per use or scheduled calibration periods preset by the manufacturer or user prompt, to a single calibration upon leaving the factory, or a combination thereof. The present application does not require network or cloud access however can benefit from having such access for storing or processing data, for example storage of images, accessing dimension data, remote processing, etc.


The disclosed embodiments are capable of processing sets of image pairs independently, providing better results than the standard techniques. The disclosed methods can be done without a strict sequential requirement, unlike SFM, which requires a sequence of 20-100 image pairs. Further, they are unique when compared with other known processes and solutions in that they (1) reduce the computational demand on the system, and (2) reduce the number of images needed to calibrate the parameters.


Unless otherwise defined, all technical or/and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application pertains.


While this disclosure describes a limited number of embodiments, it will be appreciated that many variations, modifications and other applications of such embodiments may be made. In general, the disclosure is to be understood as not limited by the specific embodiments described herein, but only by the scope of the appended claims.

Claims
  • 1. A method for stereo calibration of a dual-camera that includes a first camera and a second camera, the method comprising: a) obtaining optimized extrinsic and intrinsic parameters using initial intrinsic parameters and, optionally, initial extrinsic parameters, by obtaining with the dual-camera a set of left and right images, matching corresponding points in the left and right images to obtain matched feature points, calculating extrinsic parameters, and refining the initial intrinsic parameters based on the matched feature points;b) estimating an infinity offset e using the optimized extrinsic and extrinsic parameters;c) estimating a scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset e; andd) using the optimized extrinsic and extrinsic parameters, infinity offset e and scaling factor s to provide stereo calibration that leads to improved depth estimation.
  • 2. The method of claim 1, wherein the stereo calibration includes dynamic stereo calibration.
  • 3. The method of claim 2, wherein the dynamic stereo calibration is performed in a moving vehicle that includes the dual-camera.
  • 4. The method of claim 1, wherein the initial intrinsic parameters include nominal values of intrinsic parameters of the first and second cameras.
  • 5. The method of claim 1, wherein the initial intrinsic parameters include factory calibrated initial intrinsic parameters of the first and second cameras.
  • 6. The method of claim 1, wherein the initial intrinsic parameters include initial intrinsic parameters of the first and second cameras estimated independently from bundle adjustment.
  • 7. The method of claim 1, wherein the initial intrinsic parameters include initial intrinsic parameters of the first and second cameras estimated independently from structure from motion.
  • 8. The method of claim 1, further comprising iterating between the calculating extrinsic parameters and the matching corresponding points in the left and right images until sufficient matched points are gathered.
  • 9. The method of claim 1, wherein the refining the initial intrinsic parameters includes calculating a difference in image location for each matched feature point along a non-disparity axis and wherein the obtaining optimized intrinsic parameters include fulfilling a stop condition.
  • 10. The method of claim 8, wherein the refining the initial intrinsic parameters includes calculating a difference in image location for each matched feature point along a non-disparity axis and wherein the obtaining optimized intrinsic parameters include fulfilling a stop condition.
  • 11. A method for stereo calibration of a dual-camera that includes a first camera and a second camera, comprising: obtaining optimized extrinsic and intrinsic parameters using initial intrinsic parameters and, optionally, initial extrinsic parameters by obtaining dynamically at least two sets of stereo images, wherein each stereo image set includes a left image and a right image, matching corresponding points in left and right images of each set, matching corresponding points in, respectively, left images and right images of at least two sets, generating a disparity map by calculating disparity values in the two time frames for all features matched in same sets and between sets, labeling pixels with constant disparity over different time steps as respective points at infinity and estimating infinity offset e, from a respective disparity of the points at infinity,estimating an infinity offset e using the optimized extrinsic and extrinsic parameters;estimating a scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset e; andusing the optimized extrinsic and extrinsic parameters, infinity offset e and scaling factor s to provide stereo calibration that leads to improved depth estimation.
  • 12. A method for stereo calibration of a dual-camera that includes a first camera and a second camera, the method comprising: obtaining optimized extrinsic and intrinsic parameters using initial intrinsic parameters and, optionally, initial extrinsic parameters,estimating an infinity offset e using the optimized extrinsic and extrinsic parameters;estimating a scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset e by obtaining dynamically at least one set of stereo images, detecting in the set at least one object of known dimensions (OKD) to obtain a detected OKD marked XOKD, matching corresponding points in XOKD, calculating a size of XOKD, calculating a disparity of XOKD, calculating a distance of XOKD from the dual-camera and estimating scaling factor s using the size, the disparity and the distance; andusing the optimized extrinsic and extrinsic parameters, infinity offset e and scaling factor s to provide stereo calibration that leads to improved depth estimation.
  • 13. The method of claim 2, wherein the estimating scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset e includes obtaining dynamically at least one set of stereo images, detecting in the set at least one object of known dimensions (OKD) to obtain a detected OKD marked XOKD, matching corresponding points in XOKD, calculating a size of XOKD, calculating a disparity of XOKD, calculating a distance of XOKD from the dual-camera and estimating scaling factor s using the size, the disparity and the distance.
  • 14. The method of claim 3, wherein the estimating scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset e includes obtaining dynamically at least one set of stereo images, detecting in the set at least one object of known dimensions (OKD) to obtain a detected OKD marked XOKD, matching corresponding points in XOKD, calculating a size of XOKD, calculating a disparity of XOKD, calculating a distance of XOKD from the dual-camera and estimating scaling factor s using the size, the disparity and the distance.
  • 15. The method of claim 11, wherein the estimating scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset e includes obtaining dynamically at least one set of stereo images, detecting in the set at least one object of known dimensions (OKD) to obtain a detected OKD marked XOKD, matching corresponding points in XOKD, calculating a size of XOKD, calculating a disparity of XOKD, calculating a distance of XOKD from the dual-camera and estimating scaling factor s using the size, the disparity and the distance.
  • 16. A method for stereo calibration of a dual-camera that includes a first camera and a second camera, the method comprising: obtaining optimized extrinsic and intrinsic parameters using initial intrinsic parameters and, optionally, initial extrinsic parameters,estimating an infinity offset e using the optimized extrinsic and extrinsic parameters;estimating a scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset e by estimating scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset e includes obtaining dynamically at least two sets of stereo images, detecting in the sets at least one stationary object XOS, matching corresponding points in XOS to obtain a disparity, obtaining a distance driven between the obtaining of the at least two sets, and estimating scaling factor s using the disparity and the distance; andusing the optimized extrinsic and extrinsic parameters, infinity offset e and scaling factor s to provide stereo calibration that leads to improved depth estimation.
  • 17. The method of claim 2, wherein the estimating scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset e includes obtaining dynamically at least two sets of stereo images, detecting in the sets at least one stationary object XOS, matching corresponding points in XOS to obtain a disparity, obtaining a distance driven between the obtaining of the at least two sets, and estimating scaling factor s using the disparity and the distance.
  • 18. The method of claim 3, wherein the estimating scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset e includes obtaining dynamically at least two sets of stereo images, detecting in the sets at least one stationary object XOS, matching corresponding points in XOS to obtain a disparity, obtaining a distance driven between the obtaining of the at least two sets, and estimating scaling factor s using the disparity and the distance.
  • 19. The method of claim 11, wherein the estimating scaling factor s using the optimized extrinsic and extrinsic parameters and infinity offset e includes obtaining dynamically at least two sets of stereo images, detecting in the sets at least one stationary object XOS, matching corresponding points in XOS to obtain a disparity, obtaining a distance driven between the obtaining of the at least two sets, and estimating scaling factor s using the disparity and the distance.
CROSS REFERENCE TO RELATED APPLICATIONS

This application is a 371 application from international patent application PCT/IB2020/051948 filed Mar. 6, 2020, and is related to and claims priority from U.S. Provisional Patent Application No. 62/816,097 filed on Mar. 9, 2019, which is expressly incorporated herein by reference in its entirety.

PCT Information
Filing Document Filing Date Country Kind
PCT/IB2020/051948 3/6/2020 WO 00
Publishing Document Publishing Date Country Kind
WO2020/183312 9/17/2020 WO A
US Referenced Citations (300)
Number Name Date Kind
3085354 Rasmussen et al. Apr 1963 A
3584513 Gates Jun 1971 A
3941001 LaSarge Mar 1976 A
4199785 McCullough et al. Apr 1980 A
5005083 Grage et al. Apr 1991 A
5032917 Aschwanden Jul 1991 A
5041852 Misawa et al. Aug 1991 A
5051830 von Hoessle Sep 1991 A
5099263 Matsumoto et al. Mar 1992 A
5248971 Mandl Sep 1993 A
5287093 Amano et al. Feb 1994 A
5394520 Hall Feb 1995 A
5436660 Sakamoto Jul 1995 A
5444478 Lelong et al. Aug 1995 A
5459520 Sasaki Oct 1995 A
5657402 Bender et al. Aug 1997 A
5682198 Katayama et al. Oct 1997 A
5768443 Michael et al. Jun 1998 A
5926190 Turkowski et al. Jul 1999 A
5940641 McIntyre et al. Aug 1999 A
5982951 Katayama et al. Nov 1999 A
6101334 Fantone Aug 2000 A
6128416 Oura Oct 2000 A
6148120 Sussman Nov 2000 A
6201533 Rosenberg et al. Mar 2001 B1
6208765 Bergen Mar 2001 B1
6268611 Pettersson et al. Jul 2001 B1
6549215 Jouppi Apr 2003 B2
6611289 Yu et al. Aug 2003 B1
6643416 Daniels et al. Nov 2003 B1
6650368 Doron Nov 2003 B1
6680748 Monti Jan 2004 B1
6714665 Hanna et al. Mar 2004 B1
6724421 Glatt Apr 2004 B1
6738073 Park et al. May 2004 B2
6741250 Furlan et al. May 2004 B1
6750903 Miyatake et al. Jun 2004 B1
6778207 Lee et al. Aug 2004 B1
7002583 Rabb, III Feb 2006 B2
7015954 Foote et al. Mar 2006 B1
7038716 Klein et al. May 2006 B2
7199348 Olsen et al. Apr 2007 B2
7206136 Labaziewicz et al. Apr 2007 B2
7248294 Slatter Jul 2007 B2
7256944 Labaziewicz et al. Aug 2007 B2
7305180 Labaziewicz et al. Dec 2007 B2
7339621 Fortier Mar 2008 B2
7346217 Gold, Jr. Mar 2008 B1
7365793 Cheatle et al. Apr 2008 B2
7411610 Doyle Aug 2008 B2
7424218 Baudisch et al. Sep 2008 B2
7509041 Hosono Mar 2009 B2
7533819 Barkan et al. May 2009 B2
7619683 Davis Nov 2009 B2
7738016 Toyofuku Jun 2010 B2
7773121 Huntsberger et al. Aug 2010 B1
7809256 Kuroda et al. Oct 2010 B2
7880776 LeGall et al. Feb 2011 B2
7918398 Li et al. Apr 2011 B2
7964835 Olsen et al. Jun 2011 B2
7978239 Deever et al. Jul 2011 B2
8115825 Culbert et al. Feb 2012 B2
8149327 Lin et al. Apr 2012 B2
8154610 Jo et al. Apr 2012 B2
8238695 Davey et al. Aug 2012 B1
8274552 Dahi et al. Sep 2012 B2
8390729 Long et al. Mar 2013 B2
8391697 Cho et al. Mar 2013 B2
8400555 Georgiev et al. Mar 2013 B1
8439265 Ferren et al. May 2013 B2
8446484 Muukki et al. May 2013 B2
8483452 Ueda et al. Jul 2013 B2
8514491 Duparre Aug 2013 B2
8547389 Hoppe et al. Oct 2013 B2
8553106 Scarff Oct 2013 B2
8587691 Takane Nov 2013 B2
8619148 Watts et al. Dec 2013 B1
8803990 Smith Aug 2014 B2
8896655 Mauchly et al. Nov 2014 B2
8976255 Matsuoto et al. Mar 2015 B2
9019387 Nakano Apr 2015 B2
9025073 Attar et al. May 2015 B2
9025077 Attar et al. May 2015 B2
9041835 Honda May 2015 B2
9137447 Shibuno Sep 2015 B2
9185291 Shabtay et al. Nov 2015 B1
9215377 Sokeila et al. Dec 2015 B2
9215385 Luo Dec 2015 B2
9270875 Brisedoux et al. Feb 2016 B2
9286680 Jiang et al. Mar 2016 B1
9344626 Silverstein et al. May 2016 B2
9360671 Zhou Jun 2016 B1
9369621 Malone et al. Jun 2016 B2
9413930 Geerds Aug 2016 B2
9413984 Attar et al. Aug 2016 B2
9420180 Jin Aug 2016 B2
9438792 Nakada et al. Sep 2016 B2
9485432 Medasani et al. Nov 2016 B1
9578257 Attar et al. Feb 2017 B2
9618748 Munger et al. Apr 2017 B2
9681057 Attar et al. Jun 2017 B2
9723220 Sugie Aug 2017 B2
9736365 Laroia Aug 2017 B2
9736391 Du et al. Aug 2017 B2
9768310 Ahn et al. Sep 2017 B2
9800798 Ravirala et al. Oct 2017 B2
9851803 Fisher et al. Dec 2017 B2
9894287 Qian et al. Feb 2018 B2
9900522 Lu Feb 2018 B2
9927600 Goldenberg et al. Mar 2018 B2
20020005902 Yuen Jan 2002 A1
20020030163 Zhang Mar 2002 A1
20020063711 Park et al. May 2002 A1
20020075258 Park et al. Jun 2002 A1
20020122113 Foote Sep 2002 A1
20020167741 Koiwai et al. Nov 2002 A1
20030030729 Prentice et al. Feb 2003 A1
20030093805 Gin May 2003 A1
20030156751 Lee et al. Aug 2003 A1
20030160886 Misawa et al. Aug 2003 A1
20030202113 Yoshikawa Oct 2003 A1
20040008773 Itokawa Jan 2004 A1
20040012683 Yamasaki et al. Jan 2004 A1
20040017386 Liu et al. Jan 2004 A1
20040027367 Pilu Feb 2004 A1
20040061788 Bateman Apr 2004 A1
20040141065 Hara et al. Jul 2004 A1
20040141086 Mihara Jul 2004 A1
20040240052 Minefuji et al. Dec 2004 A1
20050013509 Samadani Jan 2005 A1
20050046740 Davis Mar 2005 A1
20050157184 Nakanishi et al. Jul 2005 A1
20050168834 Matsumoto et al. Aug 2005 A1
20050185049 Iwai et al. Aug 2005 A1
20050200718 Lee Sep 2005 A1
20060054782 Olsen et al. Mar 2006 A1
20060056056 Ahiska et al. Mar 2006 A1
20060067672 Washisu et al. Mar 2006 A1
20060102907 Lee et al. May 2006 A1
20060125937 LeGall et al. Jun 2006 A1
20060170793 Pasquarette et al. Aug 2006 A1
20060175549 Miller et al. Aug 2006 A1
20060187310 Janson et al. Aug 2006 A1
20060187322 Janson et al. Aug 2006 A1
20060187338 May et al. Aug 2006 A1
20060227236 Pak Oct 2006 A1
20070024737 Nakamura et al. Feb 2007 A1
20070126911 Nanjo Jun 2007 A1
20070177025 Kopet et al. Aug 2007 A1
20070188653 Pollock et al. Aug 2007 A1
20070189386 Imagawa et al. Aug 2007 A1
20070257184 Olsen et al. Nov 2007 A1
20070285550 Son Dec 2007 A1
20080017557 Witdouck Jan 2008 A1
20080024614 Li et al. Jan 2008 A1
20080025634 Border et al. Jan 2008 A1
20080030592 Border et al. Feb 2008 A1
20080030611 Jenkins Feb 2008 A1
20080084484 Ochi et al. Apr 2008 A1
20080106629 Kurtz et al. May 2008 A1
20080117316 Orimoto May 2008 A1
20080123937 Arias Estrada May 2008 A1
20080129831 Cho et al. Jun 2008 A1
20080218611 Parulski et al. Sep 2008 A1
20080218612 Border et al. Sep 2008 A1
20080218613 Janson et al. Sep 2008 A1
20080219654 Border et al. Sep 2008 A1
20090086074 Li et al. Apr 2009 A1
20090109556 Shimizu et al. Apr 2009 A1
20090122195 Van Baar et al. May 2009 A1
20090122406 Rouvinen et al. May 2009 A1
20090128644 Camp et al. May 2009 A1
20090219547 Kauhanen et al. Sep 2009 A1
20090252484 Hasuda et al. Oct 2009 A1
20090295949 Ojala Dec 2009 A1
20090324135 Kondo et al. Dec 2009 A1
20100013906 Border et al. Jan 2010 A1
20100020221 Tupman et al. Jan 2010 A1
20100060746 Olsen et al. Mar 2010 A9
20100097444 Lablans Apr 2010 A1
20100103194 Chen et al. Apr 2010 A1
20100165131 Makimoto et al. Jul 2010 A1
20100196001 Ryynänen et al. Aug 2010 A1
20100238327 Griffith et al. Sep 2010 A1
20100259836 Kang et al. Oct 2010 A1
20100283842 Guissin et al. Nov 2010 A1
20100321494 Peterson et al. Dec 2010 A1
20110058320 Kim et al. Mar 2011 A1
20110063417 Peters et al. Mar 2011 A1
20110063446 McMordie et al. Mar 2011 A1
20110064327 Dagher et al. Mar 2011 A1
20110080487 Venkataraman et al. Apr 2011 A1
20110128288 Petrou et al. Jun 2011 A1
20110164172 Shintani et al. Jul 2011 A1
20110229054 Weston et al. Sep 2011 A1
20110234798 Chou Sep 2011 A1
20110234853 Hayashi et al. Sep 2011 A1
20110234881 Wakabayashi et al. Sep 2011 A1
20110242286 Pace et al. Oct 2011 A1
20110242355 Goma et al. Oct 2011 A1
20110298966 Kirschstein et al. Dec 2011 A1
20120026366 Golan et al. Feb 2012 A1
20120044372 Cote et al. Feb 2012 A1
20120062780 Morihisa Mar 2012 A1
20120069235 Imai Mar 2012 A1
20120075489 Nishihara Mar 2012 A1
20120105579 Jeon et al. May 2012 A1
20120124525 Kang May 2012 A1
20120154547 Aizawa Jun 2012 A1
20120154614 Moriya et al. Jun 2012 A1
20120196648 Havens et al. Aug 2012 A1
20120229663 Nelson et al. Sep 2012 A1
20120249815 Bohn et al. Oct 2012 A1
20120287315 Huang et al. Nov 2012 A1
20120320467 Baik et al. Dec 2012 A1
20130002928 Imai Jan 2013 A1
20130016427 Sugawara Jan 2013 A1
20130063629 Webster et al. Mar 2013 A1
20130076922 Shihoh et al. Mar 2013 A1
20130093842 Yahata Apr 2013 A1
20130094126 Rappoport et al. Apr 2013 A1
20130113894 Mirlay May 2013 A1
20130135445 Dahi et al. May 2013 A1
20130155176 Paripally et al. Jun 2013 A1
20130182150 Asakura Jul 2013 A1
20130201360 Song Aug 2013 A1
20130202273 Ouedraogo et al. Aug 2013 A1
20130235224 Park et al. Sep 2013 A1
20130250150 Malone et al. Sep 2013 A1
20130258044 Betts-LaCroix Oct 2013 A1
20130270419 Singh et al. Oct 2013 A1
20130278785 Nomura et al. Oct 2013 A1
20130321668 Kamath Dec 2013 A1
20140009631 Topliss Jan 2014 A1
20140049615 Uwagawa Feb 2014 A1
20140118584 Lee et al. May 2014 A1
20140192238 Attar et al. Jul 2014 A1
20140192253 Laroia Jul 2014 A1
20140218587 Shah Aug 2014 A1
20140313316 Olsson et al. Oct 2014 A1
20140362242 Takizawa Dec 2014 A1
20150002683 Hu et al. Jan 2015 A1
20150042870 Chan et al. Feb 2015 A1
20150070781 Cheng et al. Mar 2015 A1
20150092066 Geiss et al. Apr 2015 A1
20150103147 Ho et al. Apr 2015 A1
20150124059 Georgiev et al. May 2015 A1
20150138381 Ahn May 2015 A1
20150145965 Livyatan et al. May 2015 A1
20150154776 Zhang et al. Jun 2015 A1
20150162048 Hirata et al. Jun 2015 A1
20150195458 Nakayama et al. Jul 2015 A1
20150215516 Dolgin Jul 2015 A1
20150237280 Choi et al. Aug 2015 A1
20150242994 Shen Aug 2015 A1
20150244906 Wu et al. Aug 2015 A1
20150253543 Mercado Sep 2015 A1
20150253647 Mercado Sep 2015 A1
20150261299 Wajs Sep 2015 A1
20150271471 Hsieh et al. Sep 2015 A1
20150281678 Park et al. Oct 2015 A1
20150286033 Osborne Oct 2015 A1
20150316744 Chen Nov 2015 A1
20150334309 Peng et al. Nov 2015 A1
20160044250 Shabtay et al. Feb 2016 A1
20160070088 Koguchi Mar 2016 A1
20160154202 Wippermann et al. Jun 2016 A1
20160154204 Lim et al. Jun 2016 A1
20160212358 Shikata Jul 2016 A1
20160212418 Demirdjian et al. Jul 2016 A1
20160241751 Park Aug 2016 A1
20160291295 Shabtay et al. Oct 2016 A1
20160295112 Georgiev et al. Oct 2016 A1
20160301840 Du et al. Oct 2016 A1
20160353008 Osborne Dec 2016 A1
20160353012 Kao et al. Dec 2016 A1
20170019616 Zhu et al. Jan 2017 A1
20170070731 Darling et al. Mar 2017 A1
20170187962 Lee et al. Jun 2017 A1
20170214846 Du et al. Jul 2017 A1
20170214866 Zhu et al. Jul 2017 A1
20170242225 Fiske Aug 2017 A1
20170287169 Garcia Oct 2017 A1
20170289458 Song et al. Oct 2017 A1
20180013944 Evans, V et al. Jan 2018 A1
20180017844 Yu et al. Jan 2018 A1
20180024329 Goldenberg et al. Jan 2018 A1
20180059379 Chou Mar 2018 A1
20180120674 Avivi et al. May 2018 A1
20180150973 Tang et al. May 2018 A1
20180176426 Wei et al. Jun 2018 A1
20180198897 Tang et al. Jul 2018 A1
20180241922 Baldwin et al. Aug 2018 A1
20180295292 Lee et al. Oct 2018 A1
20180300901 Wakai et al. Oct 2018 A1
20180329281 Ye Nov 2018 A1
20190121103 Bachar et al. Apr 2019 A1
20200342652 Rowell Oct 2020 A1
20210006725 Niezrecki Jan 2021 A1
20210156678 Grauzinis May 2021 A1
Foreign Referenced Citations (41)
Number Date Country
101276415 Oct 2008 CN
201514511 Jun 2010 CN
102739949 Oct 2012 CN
103024272 Apr 2013 CN
103841404 Jun 2014 CN
1536633 Jun 2005 EP
1780567 May 2007 EP
2523450 Nov 2012 EP
S59191146 Oct 1984 JP
04211230 Aug 1992 JP
H07318864 Dec 1995 JP
08271976 Oct 1996 JP
2002010276 Jan 2002 JP
2003298920 Oct 2003 JP
2004133054 Apr 2004 JP
2004245982 Sep 2004 JP
2005099265 Apr 2005 JP
2006238325 Sep 2006 JP
2007228006 Sep 2007 JP
2007306282 Nov 2007 JP
2008076485 Apr 2008 JP
2010204341 Sep 2010 JP
2011085666 Apr 2011 JP
2012132739 Jul 2012 JP
2013106289 May 2013 JP
2016105577 Jun 2016 JP
20070005946 Jan 2007 KR
20090058229 Jun 2009 KR
20100008936 Jan 2010 KR
20140014787 Feb 2014 KR
101477178 Dec 2014 KR
20140144126 Dec 2014 KR
20150118012 Oct 2015 KR
2000027131 May 2000 WO
2004084542 Sep 2004 WO
2006008805 Jan 2006 WO
2010122841 Oct 2010 WO
2014072818 May 2014 WO
2017025822 Feb 2017 WO
2017037688 Mar 2017 WO
2018130898 Jul 2018 WO
Non-Patent Literature Citations (19)
Entry
Office Action in related KR patent application 2020-7024468, dated Feb. 3, 2021.
Office Action in related EP patent application 20770621.9, dated Jun. 25, 2021.
Statistical Modeling and Performance Characterization of a Real-Time Dual Camera Surveillance System, Greienhagen et al., Publisher: IEEE, 2000, 8 pages.
A 3MPixel Multi-Aperture Image Sensor with 0.7μm Pixels in 0.11μm CMOS, Fife et al., Stanford University, 2008, 3 pages.
Dual camera intelligent sensor for high definition 360 degrees surveillance, Scotti et al., Publisher: IET, May 9, 2000, 8 pages.
Dual-sensor foveated imaging system, Hua et al., Publisher: Optical Society of America, Jan. 14, 2008, 11 pages.
Defocus Video Matting, McGuire et al., Publisher: ACM SIGGRAPH, Jul. 31, 2005, 11 pages.
Compact multi-aperture imaging with high angular resolution, Santacana et al., Publisher: Optical Society of America, 2015, 10 pages.
Multi-Aperture Photography, Green et al., Publisher: Mitsubishi Electric Research Laboratories, Inc., Jul. 2007, 10 pages.
Multispectral Bilateral Video Fusion, Bennett et al., Publisher: IEEE, May 2007, 10 pages.
Super-resolution imaging using a camera array, Santacana et al., Publisher: Optical Society of America, 2014, 6 pages.
Optical Splitting Trees for High-Precision Monocular Imaging, McGuire et al., Publisher: IEEE, 2007, 11 pages.
High Performance Imaging Using Large Camera Arrays, Wilburn et al., Publisher: Association for Computing Machinery, Inc., 2005, 12 pages.
Real-time Edge-Aware Image Processing with the Bilateral Grid, Chen et al., Publisher: ACM SIGGRAPH, 2007, 9 pages.
Superimposed multi-resolution imaging, Carles et al., Publisher: Optical Society of America, 2017, 13 pages.
Viewfinder Alignment, Adams et al., Publisher: EUROGRAPHICS, 2008, 10 pages.
Dual-Camera System for Multi-Level Activity Recognition, Bodor et al., Publisher: IEEE, Oct. 2014, 6 pages.
Engineered to the task: Why camera-phone cameras are different, Giles Humpston, Publisher: Solid State Technology, Jun. 2009, 3 pages.
International Search Report and Written Opinion in related PCT application PCT/IB2020/051948, dated Jul. 24, 2020.
Related Publications (1)
Number Date Country
20210090281 A1 Mar 2021 US
Provisional Applications (1)
Number Date Country
62816097 Mar 2019 US