Patent Application
20130107065
A common problem in video analysis is to differentiate a stationary object from moving objects. Competent video analysis relies on the ability to differentiate stationary objects (e.g., background object) from moving objects (usually in the foreground). Numerous techniques exist to perform background subtraction based on image processing algorithms. However, many of these techniques suffer from an inherent limitation of relying on the size of the moving object being small in comparison to the complete image. Traditional approaches also suffer from an inherent limitation that they cannot distinguish between camera motion and subject motion.
Finding a stationary object from a non-stationary camera is applicable when considering mobile device videos that are subject to continuous unintentional tremor and occasional panning. It is also equally applicable when the camera is mounted on a mobile platform like a robot, a plane, an unmanned aerial vehicle (UAV), etc. In most cases, the section of the image that is stationary is the background and techniques described herein offer a powerful method to achieve background identification and subtraction. Ability to discover stationary objects using the techniques described herein have numerous applications like surveillance, intruder detection and image stabilization.
Embodiments of the invention address these and other problems.
Techniques for identifying stationary objects are provided, herein. Integrated inertial MEMS sensors have recently made their way onto low-cost consumer cameras and cellular phone cameras and provide an effective way to address this problem. Gyroscopes, accelerometers and magnetometers are examples of such inertial sensors that may be used in embodiments of the invention. Gyroscopes measure the angular velocity of the camera along three axes and accelerometers measure both the acceleration due to gravity and the dynamic acceleration of the camera along three axes. These sensors provide a good measure of the movement of the camera when held by a user. This includes movements caused by panning as well as unintentional tremor.
The movement of the camera causes shifts in the image captured. Known image processing techniques may be used to track the shift in the image on a frame-by-frame basis. The movement of the camera is also tracked using inertial sensors like gyroscopes. The expected image shift due to the camera motion (as measured by the inertial sensors), is calculated by appropriately scaling the camera movement taking into account the camera's focal length, pixel pitch, etc.
Some regions of the image may show strong similarity between the inertial sensor estimated image shift and the image shift calculated by known image processing techniques. The portions of the image may be defined as sub-frames of the image or individual fine grained features identified and described by using known techniques such as scale invariant feature transform (SIFT). By calculating the degree of similarity between the image shift as predicted by known image processing techniques with that estimated using an inertial sensor, the device can estimate the regions of the image that are stationary and those that are moving, discounting the motion or shift introduced by the motion of the camera.
An example of a method for identifying a stationary portion of an image may include obtaining a sequence of images using a camera, identifying multiple portions of an image from the sequence of images, detecting a shift associated with each of the multiple portions of the image, detecting a motion using a sensor mechanically coupled to the camera, deriving a projected shift for the image based on the detected motion of the camera using the sensor, comparing the projected shift associated with the motion using the sensor with the shift associated with each portion of the image, and identifying a portion of the image that is most similar to the projected shift associated with the motion detected using the sensor, as the stationary portion of the image. Identifying multiple portions of the image may include identifying multiple features from the image. The sequence of images may belong to a video stream.
In some embodiments, detecting the shift associated with each of the multiple portions of the image may include associating, from the image, one or more portions of the image with a same relative location in the one or more other images from the sequence of images to generate a sequence of portions from the images, and determining the shift associated with the one or more portions of the image using deviations in a plurality of pixels in the sequence of portions from the images. In other implementations, detecting the shift associated with each of the multiple portions of the sequence of images may entail analyzing the similarly situated corresponding portions throughout the sequence of images.
In some implementations, a projected shift for the image is derived using a scaled value of the motion detected from the sensor. The sensors used may be inertial sensors that are one or more from a group comprising of gyroscope, an accelerometer and a magnetometer. The shift in the image may be from movement of the camera obtaining the image or by an object in the field of view of the camera.
The shift of different portions in the image may be correlated with the motion detected using the sensor. In some situations, the camera may be non-stationary and attached to device. In some aspects, the similarity in the motion of the different portions of the image and the motion as calculated by the sensor is calculated as a correlation between the input from the camera and the input from the sensor. Identifying stationary portions of the image may be used for surveillance, moving object detection, intruder detection in videos, and video and image stabilization.
An example device implementing the method may include a processor, a camera for obtaining images, a sensor for detecting motion associated with the device, and a non-transitory computer-readable storage medium coupled to the processor. The non-transitory computer-readable storage medium comprises code executable by the processor for implementing a method that includes obtaining a sequence of images using the camera, identifying multiple portions of an image from the sequence of images, detecting a shift associated with each of the multiple portions of the image, detecting a motion using the sensor mechanically coupled to the camera, deriving a projected shift for the image based on the detected motion of the camera using the sensor, comparing the projected shift associated with the motion using the sensor with the shift associated with each portion of the image, and identifying a portion of the image that is most similar to the projected shift associated with the motion detected using the sensor, as a stationary portion of the image. Identifying multiple portions of the image may include identifying multiple features from the image. The sequence of images may belong to a video stream.
Implementations of such a device may include detecting the shift associated with each of the multiple portions of the image that includes associating, from the image, one or more portions of the image with a same relative location in the one or more other images from the sequence of images to generate a sequence of portions from the images, and determining the shift associated with the one or more portions of the image using deviations in a plurality of pixels in the sequence of portions from the images. Other implementations of such a device may include detecting the shift associated with each of the multiple portions of the sequence of images which comprises analyzing the similarly situated corresponding portions throughout the sequence of images.
In some implementations, the device derives a projected shift for the image using a scaled value of the motion detected from the sensor. The sensors coupled to the device may be inertial sensors that are one or more from a group comprising of gyroscope, an accelerometer and a magnetometer. The shift in the image may be from movement of the camera obtaining the image or by an object in the field of view of the camera.
In some implementations, the device may correlate the shift of different portions in the image with the motion detected using the sensor. In some situations, the camera may be non-stationary and attached to device. In some aspects, the similarity in the motion of the different portions of the image and the motion as calculated by the sensor is calculated as a correlation between the input from the camera and the input from the sensor. Identifying stationary portions of the image may be used for surveillance, moving object detection, intruder detection in videos and video, and image stabilization.
An example non-transitory computer-readable storage medium coupled to a processor, wherein the non-transitory computer-readable storage medium comprises a computer program executable by the processor for implementing a method, includes obtaining a sequence of images using a camera; identifying multiple portions of an image from the sequence of images; detecting a shift associated with each of the multiple portions of the image; detecting a motion using a sensor mechanically coupled to the camera; deriving a projected shift for the image based on the detected motion of the camera using the sensor; comparing the projected shift associated with the motion using the sensor with the shift associated with each portion of the image; and identifying a portion of the image that may be most similar to the projected shift associated with the motion detected using the sensor, as a stationary portion of the image.
Implementations of such a non-transitory computer-readable storage medium may include detecting the shift associated with each of the multiple portions of the image that includes associating, from the image, one or more portions of the image with a same relative location in the one or more other images from the sequence of images to generate a sequence of portions from the images, and determining the shift associated with the one or more portions of the image using deviations in a plurality of pixels in the sequence of portions from the images. Other implementations of such a device may include detecting the shift associated with each of the multiple portions of the sequence of images which comprises analyzing the similarly situated corresponding portions throughout the sequence of images.
Implementations of such a non-transitory computer-readable storage medium may include one or more of the following features. In some implementations, the non-transitory computer-readable storage medium derives a projected shift for the image using a scaled value of the motion detected from the sensor. The sensors coupled to the device may be inertial sensors that are one or more from a group comprising of gyroscope, an accelerometer and a magnetometer. The shift in the image may be from movement of the camera obtaining the image or by an object in the field of view of the camera.
In some implementations, the non-transitory computer-readable storage medium may correlate the shift of different portions in the image with the motion detected using the sensor. In some situations, the camera may be non-stationary and attached to device. In some aspects, the similarity in the motion of the different portions of the image and the motion as calculated by the sensor is calculated as a correlation between the input from the camera and the input from the sensor. Identifying stationary portions of the image may be used for surveillance, moving object detection, intruder detection in videos, and video and image stabilization.
An example apparatus performing a method for identifying a stationary portion of an image may include means for obtaining a sequence of images using a camera, means for identifying multiple portions of an image from the sequence of images, means for detecting a shift associated with each of the multiple portions of the image, means for detecting a motion using a sensor mechanically coupled to the camera, means for deriving a projected shift for the image based on the detected motion of the camera using the sensor, means for comparing the projected shift associated with the motion using the sensor with the shift associated with each portion of the image, and means for identifying a portion of the image that is most similar to the projected shift associated with the motion detected using the sensor, as the stationary portion of the image. Identifying multiple portions of the image may include identifying multiple features from the image. The sequence of images may belong to a video stream.
In the above described example apparatus, detecting the shift associated with each of the multiple portions of the image may include means for associating, from the image, one or more portions of the image with a same relative location in the one or more other images from the sequence of images to generate a sequence of portions from the images, and means for determining the shift associated with the one or more portions of the image using deviations in a plurality of pixels in the sequence of portions from the images. In another implementation of the apparatus, detecting the shift associated with each of the multiple portions of the sequence of images comprises a means for analyzing the similarly situated corresponding portions throughout the sequence of images.
In some implementations of the apparatus, a projected shift for the image is derived using a scaled value of the motion detected from the sensor. The sensors used may be inertial sensors that are one or more from a group comprising of gyroscope, an accelerometer and a magnetometer. The shift in the image may be from movement of the camera obtaining the image or by an object in the field of view of the camera.
The shift of different portions in the image may be correlated with the motion detected using the sensor. In some situations, the camera may be non-stationary and attached to a device. In some aspects, the similarity in the motion of the different portions of the image and the motion as calculated by the sensor is calculated using means for correlating between the input from the camera and the input from the sensor. Identifying stationary portions of the image may be used for surveillance, moving object detection, intruder detection in videos, and video and image stabilization.
The foregoing has outlined rather broadly the features and technical advantages of examples according to the disclosure in order that the detailed description that follows can be better understood. Additional features and advantages will be described hereinafter. The conception and specific examples disclosed can be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. Such equivalent constructions do not depart from the spirit and scope of the appended claims. Features which are believed to be characteristic of the concepts disclosed herein, both as to their organization and method of operation, together with associated advantages, will be better understood from the following description when considered in connection with the accompanying figures. Each of the figures is provided for the purpose of illustration and description only and not as a definition of the limits of the claims.
The following description is provided with reference to the drawings, where like reference numerals are used to refer to like elements throughout. While various details of one or more techniques are described herein, other techniques are also possible. In some instances, well-known structures and devices are shown in block diagram form in order to facilitate describing various techniques.
A further understanding of the nature and advantages of examples provided by the disclosure can be realized by reference to the remaining portions of the specification and the drawings, wherein like reference numerals are used throughout the several drawings to refer to similar components. In some instances, a sub-label is associated with a reference numeral to denote one of multiple similar components. When reference is made to a reference numeral without specification to an existing sub-label, the reference numeral refers to all such similar components.
A common problem in video analysis is to differentiate a stationary object from moving objects (usually in the foreground). Competent video analysis relies on the ability to differentiate stationary objects (e.g., background object) from moving objects. Numerous techniques exist to perform background subtraction based on image processing algorithms. However, many of these techniques suffer from an inherent limitation of relying on the size of the moving object being small in comparison to the complete image. This may provide erroneous results where the moving object is much larger that the stationary object.
Accordingly, a technique for stationary object detection in a video provided herein utilizes inertial sensor information for improved stationary object detection. Gyroscopes, accelerometers and magnetometers are examples of such inertial sensors. Inertial sensors provide a good measure for the movement of the camera. This includes movements caused by panning as well as unintentional tremor. A video may be characterized as a sequence of images. Processing of an image, as discussed herein, may refer to processing of an image from the sequence of images of a video stream in reference to other images from the sequence of images. In some instances, the term “images” may be used interchangeably with the term “video” without departing from the scope of the invention.
The movement of the camera causes shifts in the video captured. Known image processing techniques may be used to track the shift in the video on a frame-by-frame basis. The movement of the camera is also tracked using inertial sensors like gyroscopes. The expected image shift due to the camera motion (as measured by the inertial sensors) is calculated by appropriately scaling the camera movement, taking into account the camera's focal length, pixel pitch, etc.
By calculating the degree of similarity between the image shift as predicted by known image processing techniques and that estimated using an inertial sensor, the device can estimate the regions of an image from a sequence of images that are stationary and those that are moving. Some portions of the image from the sequence of images may show strong similarity between the inertial sensor estimated image shift and the image shift calculated by known image processing techniques. The regions or portions of the image may be defined as components, objects of the image or individual fine grained features identified by using known techniques such as scale invariant feature transform (SIFT). SIFT is an algorithm in computer vision to detect and describe local features in images. For any object in an image, interesting points on the object can be extracted to provide a “feature description” of the object. This description, extracted from a training image, can then be used to identify the object when attempting to locate the object in an image containing many other objects.
The techniques described herein are also applicable when considering handheld digital cameras which are subject to continuous hand tremor and occasional panning. In most cases, the section of the image that is stationary is the background and this technique offers a method for background identification and subtraction with applications in surveillance, intruder detection and image stabilization.
Referring again to
Related image processing techniques are valuable in detecting motion associated with an image or portions of the image. However, these traditional techniques have difficulty in isolating a stationary object from a scene with a number of moving components, where the device obtaining the images contributes to the shift in the image or the video. In one aspect, additional inertial sensors coupled to the device may be used in detecting the motion associated with the device obtaining the images. One aspect of such a technique is described herein.
One or more sensors 410 are used to detect motion associated with the motion of the camera coupled to the device. The one or more sensors 410 may be coupled to the device reflecting similar motion experienced by the camera. In one aspect, the sensors are inertial sensors that include accelerometers and gyroscopes. An accelerometer measures linear acceleration and a gyroscope measures angular rate, both without an external reference. Current inertial sensor technologies are focused on MEMS technology. MEMS technology enables quartz and silicon sensors to be mass produced at low cost using etching techniques with several sensors on a single silicon wafer. MEMS sensors are small, light and exhibit much greater shock tolerance than conventional mechanical designs. However, other technologies are also being researched for more sophisticated inertial sensors, such as Micro-Optical-Electro-Mechanical-Systems (MOEMS), that remedy some of the deficiencies related to capacitive pick-up in the MEMS devices. In addition to inertial sensors, other sensors that detect motion related to acceleration, or angular rate of a body with respect to features in the environment may also be used in quantifying the motion associated with the camera.
At logical block 406, the device performs a similarity analysis between the motion associated with the device using sensors 410 coupled to the device and the motion associated with the different portions of the image detected from the image processing 404 of the sequence of images from the video. At logical block 408, one or more stationary objects in the video are detected by identifying portions from the image that are most similar with the motion detected using the sensor.
As described herein, a sequence of images is a set of images obtained one after the other by the camera coupled to the device, in that order, but are not limited to images obtained by utilizing every consecutive image in a sequence of images. For example, in detecting the motion associated with a sequence of images, from a consecutive set of images containing the set of images 1, 2, 3, 4, 5, 6, 7, 8, and 9, the image processing technique may choose to obtain or utilize the sequential images 2, 6 and 9 in determining the motion associated with different portions of the image.
In one aspect, a portion of the image may be sub-frames, wherein the sub-frames are groupings of pixels that are related by their proximity to each other, as depicted in
Motion detected using the sensor (5(A)) and motion detected using image processing techniques for each portion of the image (502) are compared to find a portion from the image which is most similar (504) to the motion detected using the sensor (5(A)). The portion of the image with the most similarity to the motion detected using the sensor is identified as the stationary portion from the image. One or more portions may be identified as stationary portions in the image. The comparison between the motion from the sensor and the motion from the portions of the image for similarity may be a correlation, sum of absolute differences or any other suitable means.
Referring back to
Referring to
At block 604, the device identifies multiple portions from an image from the sequence of images. Multiple portions from an image may be identified using a number of suitable methods. In one aspect, the image is obtained in a number of portions. In another aspect, the image is obtained and then separate portions of the image are identified. A portion of the image may be a sub-frame, wherein the sub-frames are groupings of pixels that are related by their proximity to each other, as depicted in
At block 606, the device detects a shift associated with each of the multiple portions of a sequence of images or a video. The shift detected in the image using image processing techniques is a combination of the shift due to the motion from the device capturing the video and the motion of the objects in the field of view of the camera. In one aspect, the shift associated with each of the multiple portions of the image is detected by analyzing a sequence of images. For example, from each image from a sequence of images, a portion from the image with the same relative location in the image is associated to form a sequence of portions from the images. Deviations in the sequence of portions from the images may be analyzed to determine the shift associated with that particular portion of the image. As described herein, a sequence of images is a set of images obtained one after the other, in that order, but are not limited to images obtained by utilizing every consecutive image in a sequence of images.
At block 608, the device detects motion using one or more sensors mechanically coupled to the camera. In one aspect the sensors are inertial sensors that include accelerometers and gyroscopes. An accelerometer measures linear acceleration and a gyroscope measures angular rate, both without an external reference. Current inertial sensor technologies are focused on MEMS technology. However, other technologies are also being researched for more sophisticated inertial sensors, such as Micro-Optical-Electro-Mechanical-Systems (MOEMS), that remedy some of the deficiencies related to capacitive pick-up in the MEMS devices. In addition to inertial sensors, other sensors that detect motion related to acceleration, or angular rate of a body with respect to features in the environment may also be used in quantifying the motion associated with the camera.
At block 610, the device derives a projected shift for the image based on the detected motion of the camera using the sensor. The projected image shift due to the camera motion (as measured by the inertial sensors) is calculated by appropriately scaling the camera movement taking into account the camera's focal length, pixel pitch, etc.
At block 612, the device compares the projected shift detected using the sensor with the shift associated with each portion of the image. Shift detected using the sensor and shift detected using image processing techniques for each portion of the image are compared to find a shift associated with a portion from the image which is most similar with the shift detected using the sensor. At block 614, the device identifies a portion from the image which is most similar with the motion detected using the sensor, as a stationary portion of the image. One or more portions may be identified as stationary portions in the image. The comparison between the motion from the sensor and the motion from the portions of the image for similarity may be a correlation, sum of squares or any other suitable means.
It should be appreciated that the specific steps illustrated in
A computer system as illustrated in
The computer system 700 is shown comprising hardware elements that can be electrically coupled via a bus 705 (or may otherwise be in communication, as appropriate). The hardware elements may include one or more processors 710, including without limitation one or more general-purpose processors and/or one or more special-purpose processors (such as digital signal processing chips, graphics acceleration processors, and/or the like); one or more input devices 715, which can include without limitation a camera, sensors (including inertial sensors), a mouse, a keyboard and/or the like; and one or more output devices 720, which can include without limitation a display unit, a printer and/or the like.
The computer system 700 may further include (and/or be in communication with) one or more non-transitory storage devices 725, which can comprise, without limitation, local and/or network accessible storage, and/or can include, without limitation, a disk drive, a drive array, an optical storage device, a solid-state storage device such as a random access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable and/or the like. Such storage devices may be configured to implement any appropriate data storage, including without limitation, various file systems, database structures, and/or the like.
The computer system 700 might also include a communications subsystem 730, which can include without limitation a modem, a network card (wireless or wired), an infrared communication device, a wireless communication device and/or chipset (such as a Bluetooth™ device, an 802.11 device, a WiFi device, a WiMax device, cellular communication facilities, etc.), and/or the like. The communications subsystem 730 may permit data to be exchanged with a network (such as the network described below, to name one example), other computer systems, and/or any other devices described herein. In many embodiments, the computer system 700 will further comprise a non-transitory working memory 735, which can include a RAM or ROM device, as described above.
The computer system 700 also can comprise software elements, shown as being currently located within the working memory 735, including an operating system 740, device drivers, executable libraries, and/or other code, such as one or more application programs 745, which may comprise computer programs provided by various embodiments, and/or may be designed to implement methods, and/or configure systems, provided by other embodiments, as described herein. Merely by way of example, one or more procedures described with respect to the method(s) discussed above might be implemented as code and/or instructions executable by a computer (and/or a processor within a computer); in an aspect, then, such code and/or instructions can be used to configure and/or adapt a general purpose computer (or other device) to perform one or more operations in accordance with the described methods.
A set of these instructions and/or code might be stored on a computer-readable storage medium, such as the storage device(s) 725 described above. In some cases, the storage medium might be incorporated within a computer system, such as computer system 700. In other embodiments, the storage medium might be separate from a computer system (e.g., a removable medium, such as a compact disc), and/or provided in an installation package, such that the storage medium can be used to program, configure and/or adapt a general purpose computer with the instructions/code stored thereon. These instructions might take the form of executable code, which is executable by the computer system 700 and/or might take the form of source and/or installable code, which, upon compilation and/or installation on the computer system 700 (e.g., using any of a variety of generally available compilers, installation programs, compression/decompression utilities, etc.) then takes the form of executable code.
Substantial variations may be made in accordance with specific requirements. For example, customized hardware might also be used, and/or particular elements might be implemented in hardware, software (including portable software, such as applets, etc.), or both. Further, connection to other computing devices such as network input/output devices may be employed.
Some embodiments may employ a computer system (such as the computer system 700) to perform methods in accordance with the disclosure. For example, some or all of the procedures of the described methods may be performed by the computer system 700 in response to processor 710 executing one or more sequences of one or more instructions (which might be incorporated into the operating system 740 and/or other code, such as an application program 745) contained in the working memory 735. Such instructions may be read into the working memory 735 from another computer-readable medium, such as one or more of the storage device(s) 725. Merely by way of example, execution of the sequences of instructions contained in the working memory 735 might cause the processor(s) 710 to perform one or more procedures of the methods described herein.
The terms “machine-readable medium” and “computer-readable medium,” as used herein, refer to any medium that participates in providing data that causes a machine to operate in a specific fashion. In an embodiment implemented using the computer system 700, various computer-readable media might be involved in providing instructions/code to processor(s) 710 for execution and/or might be used to store and/or carry such instructions/code (e.g., as signals). In many implementations, a computer-readable medium is a physical and/or tangible storage medium. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical and/or magnetic disks, such as the storage device(s) 725. Volatile media include, without limitation, dynamic memory, such as the working memory 735. Transmission media include, without limitation, coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 705, as well as the various components of the communications subsystem 730 (and/or the media by which the communications subsystem 730 provides communication with other devices). Hence, transmission media can also take the form of waves (including without limitation radio, acoustic and/or light waves, such as those generated during radio-wave and infrared data communications).
Common forms of physical and/or tangible computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punchcards, papertape, any other physical medium with patterns of holes, a RAM, a PROM, EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read instructions and/or code.
Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to the processor(s) 710 for execution. Merely by way of example, the instructions may initially be carried on a magnetic disk and/or optical disc of a remote computer. A remote computer might load the instructions into its dynamic memory and send the instructions as signals over a transmission medium to be received and/or executed by the computer system 700. These signals, which might be in the form of electromagnetic signals, acoustic signals, optical signals and/or the like, are all examples of carrier waves on which instructions can be encoded, in accordance with various embodiments of the invention.
The communications subsystem 730 (and/or components thereof) generally will receive the signals, and the bus 705 then might carry the signals (and/or the data, instructions, etc. carried by the signals) to the working memory 735, from which the processor(s) 710 retrieves and executes the instructions. The instructions received by the working memory 735 may optionally be stored on a non-transitory storage device 725 either before or after execution by the processor(s) 710.
The methods, systems, and devices discussed above are examples. Various embodiments may omit, substitute, or add various procedures or components as appropriate. For instance, in alternative configurations, the methods described may be performed in an order different from that described, and/or various stages may be added, omitted, and/or combined. Also, features described with respect to certain embodiments may be combined in various other embodiments. Different aspects and elements of the embodiments may be combined in a similar manner. Also, technology evolves and, thus, many of the elements are examples that do not limit the scope of the disclosure to those specific examples.
Specific details are given in the description to provide a thorough understanding of the embodiments. However, embodiments may be practiced without these specific details. For example, well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail in order to avoid obscuring the embodiments. This description provides example embodiments only, and is not intended to limit the scope, applicability, or configuration of the invention. Rather, the preceding description of the embodiments will provide those skilled in the art with an enabling description for implementing embodiments of the invention. Various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention.
Also, some embodiments were described as processes depicted as flow diagrams or block diagrams. Although each may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. A process may have additional steps not included in the figure. Furthermore, embodiments of the methods may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the associated tasks may be stored in a computer-readable medium such as a storage medium. Processors may perform the associated tasks.
Having described several embodiments, various modifications, alternative constructions, and equivalents may be used without departing from the spirit of the disclosure. For example, the above elements may merely be a component of a larger system, wherein other rules may take precedence over or otherwise modify the application of the invention. Also, a number of steps may be undertaken before, during, or after the above elements are considered. Accordingly, the above description does not limit the scope of the disclosure.
This application claims priority to U.S. Provisional Application No. 61/552,378 entitled “INERTIAL SENSOR AIDED STATIONARY OBJECT DETECTION IN VIDEOS,” filed Oct. 27, 2011 and is hereby incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 61552378 | Oct 2011 | US |
