Passive infrared (PIR) sensors may be used to detect movement of an object. PIR sensors operate by detecting changes in IR radiation to detect a moving object (human, animal, vehicle, etc.) if the object is at a different temperature than its background or surroundings.
Existing PIR sensors depend on movement of an object to detect the object. Infrared/thermal cameras may have very good resolution (e.g., 320 pixels per row) but are currently very costly and require human monitoring to distinguish different types of infrared sources. Infrared camera manufacturers continue to increase pixel counts of sensor arrays in an effort to improve image resolution for use in object or person identification applications.
Embodiments of the present invention use low-resolution thermal sensors (e.g., 32 or 8 pixels per row) in stereo (dual sensors) in combination with image processing or analytics to detect additional information about objects, even stationary objects, such as the range/distance of an object from the sensors. A signal may be output to indicate the presence or nature of a detected source. Specifically, children may be distinguished from adults or inanimate objects, enabling embodiments of the invention to be used in a wide variety of settings as a safety mechanism or feature. One advantage of using stereo infrared sensors is that the sensor can be placed in many more environments at many angles and can determine three-dimensional information about an infrared source. The combination of stereo thermal sensors and object detection analytics provides a sophisticated, versatile, and low-cost detector.
In one embodiment, an apparatus, or corresponding method, for detecting a source of infrared emission includes first and second infrared sensors configured to provide at least one first and one second image, respectively. The system also includes a processor operatively coupled to the first and second infrared sensors and configured (1) to process the first and second images in conjunction with each other to detect the presence of a source as a function of the first and second images and (2) to output a signal based on the presence of the source.
In some embodiments, the processor is further configured to determine at least one characteristic of the source based upon the first and second images and to output the signal based upon the characteristic. In embodiments in which the processor is configured to determine at least one characteristic of the source, the processor may further assign the source to a class based upon the characteristic and output the signal as a function of the class. The class may include human, animal, inanimate object, adult, or child, for example. Further, the characteristic of the source that is determined may include speed, size, height, width, temperature, or range, for example.
In some embodiments, the processor is configured to detect edges of the source in the first and second images and determine a characteristic of the source based on a combination of the edges. In some embodiments, the first and second infrared sensors have sensor dimensions of fewer pixels than are required to distinguish detailed human features. In some embodiments, the first and second infrared sensors have sensor dimensions of no greater than 300 pixels in length in either row or column axes. In some embodiments, the processor is configured to perform a noise reduction on the first and second images.
In some embodiments, the processor is configured to provide notification if the apparatus deems detection of the source is unreliable or unavailable based on an infrared signature of an environment within a field of view of the first and second sensors. In some embodiments, the first and second images include negative infrared images of the source relative to a background within a field of view of the first and second sensors. In some embodiments, the processor is configured to process the first and second images in conjunction with each other to determine a shape of the source in three dimensions, a distance of the source from the first and second infrared sensors, or both.
The foregoing will be apparent from the following more particular description of example embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments of the present invention.
A description of example embodiments of the invention follows.
The word “infrared,” as used herein, denotes the portion of the electromagnetic spectrum between visible wavelengths and microwave wavelengths, or from about 700 nanometers to about 1 millimeter. This region covers near-infrared, mid-infrared, and far-infrared wavelengths. This region of wavelengths, or at least a portion of this region, may also be referred to as “thermal” wavelengths.
Existing passive infrared (PIR) sensors detect changes in IR radiation to detect a moving object (human, animal, vehicle, etc.) that is at a different temperature from its surroundings or background. These sensors have advantages over visible optical approaches since PIR sensors use the thermal emission of an object, which is not dependent on scene lighting and is effective during the day or night. Existing sensors detect a change in IR radiation to detect a moving object, such as a human.
A disadvantage of existing PIR sensors is if the object (human, animal, vehicle, etc.) remains stationary, the sensor cannot register that an object is still in its detection area. In addition, existing PIR sensors include only a single pixel or a few pixels, limiting them to applications needing only presence information. There is no ability to provide information about the number of objects in the scene or the size of the object in the scene.
Single imager thermal solutions are limited in the possible locations in which they may be usefully installed or mounted because they must map a two-dimensional (2D) object into a three-dimensional (3D) space. If placed directly above an object, for example, a single imager cannot determine that object's height. In addition, single imager thermal solutions must be calibrated to detect the height or the width of an object/source. Traditional 2D analytic approaches require that a sensor be placed in a certain position to detect a person. It is not possible to determine accurately a distance from the sensor to an object in the field of view using traditional 2D analytic approaches.
Applicants have discovered that new low-resolution, low cost thermal imagers, such as those based on thermopile or microbolometer technology, can be used in stereo and combined with analytics to create a more sophisticated, yet low cost, detector/sensor. By utilizing low-resolution thermal imagers in stereo, and an analytic processor, a sensor can detect the height or location of a person and determine if that person is a child, for example. This new sensor can prevent accidents with automatic doors and elevators by not allowing the door to close if a child remains within the door's closing area, or if a human is too small to be detected by a traditional sensor. The new sensor can also be used in other safety applications where it is critical to know the location in space of people or appendages in order to provide an automatic stop to machinery.
This new sensor can detect the presence of children in extreme low-light conditions. This sensor can also be usefully installed in many more locations than a single imager thermal sensor. This sensor can be placed on an automobile to detect the presence of a stationary child, and notify the driver if a child is present, for example. Unlike existing PIR sensors, this sensor does not rely on motion of the object in order to detect the object. Embodiments of this invention utilize a combination of new low-resolution thermal image sensors, which can provide very accurate detection of objects when combined with analytics and 3D imaging to provide information such as distance, height, or size.
In some embodiments, the first and second infrared sensors have sensor dimensions of fewer pixels than are required to distinguish human features. In some of these embodiments, the sensor dimensions are of fewer pixels than are required to distinguish human appendages such as arms, legs, or head. In other of these embodiments, the sensor dimensions are of fewer pixels than are required to distinguish body shape. In yet other of these embodiments, the sensor dimensions are of fewer pixels than are required to distinguish small appendages such as fingers. In still other of these embodiments, the sensor dimensions are of fewer pixels than are required to distinguish facial features.
The processor in the detecting apparatus 105 in
In one example of processing the images in conjunction with each other, the first image is used to make a preliminary detection of an object, and the second image is used to confirm the detection. In another example, edges of a source object are detected in both the first and second images to determine a location or position of a feature of the object. In yet another example, as will be shown below in connection with a description of
x=b(xL′+xR′)/2(xL′−xR′)
y=b(yL′+yR′)/2(xL′−xR′)
z=bf/(xL′−xR′)
Other point(s) (not shown) on the object 216 may be similarly calculated to determine a height or width of a source, for example.
Similar calculations may be used in other embodiments, for example, to calculate a person's height. By computing the coordinates of the person's foot and comparing those coordinates to the coordinates of the person's head, it is possible to determine the person's height. Height can then be input into the analytic detection process to determine whether the person is a child.
In other embodiments, the procedure includes determining at least one characteristic of the source based upon the first and second images, and the signal is output based upon the characteristic of the source. The characteristic of the source may include a speed, size, height, width, temperature or range of the source. For example, a position of the source, or of one or more points on the source, may be calculated as shown in
In some embodiments, the processing at 372 involves detecting edges of the source in the first and second images to determine at least one characteristic of the source based on a combination of the edges. In some embodiments, the processing at 372 includes noise reduction. For example, noise reduction may be performed on the first image as shown later in conjunction with
In some embodiments, detecting the first and second infrared images at 370, 371, respectively, includes detecting negative infrared images of the source relative to a background within a field of view of the first and second sensors. Negative infrared images may be used where, for example, an environmental or background temperature is higher than a temperature of the infrared source to be detected. In this case, the source to be detected may emit infrared radiation at a lower intensity than the source's surroundings.
In other embodiments, the processing at 372 includes determining a shape of the source in three dimensions, a distance of the source from the first and second infrared sensors, or both. For example, distance of the source from the infrared sensors may be determined according to the diagram shown in
At 384, the multiple images are averaged to reduce noise in the output of the first infrared sensor. At 386, a gradient of gray level pixel values is calculated. The gradient of the image (these images are only grey scale and not color) provides input for edge detection, for example. The edges may belong to the background, as well as to the object of interest. The binarized image may be used as a mask to select only those strong edges that belong to the object of interest, such as the contour of a person's body. The coordinates of the selected edges may be used by the processor. The processor may calculate width and height of the object as seen from the sensors, and may also calculate the distance “z”.
At 388, a histogram of gray level pixel values is calculated. At 390, if the calculated histogram is bimodal, then the procedure continues to 392. If the histogram is not bimodal, then the procedure begins anew at 380. At 392, the valley between the two modes of the bimodal histogram is found, and the value of the valley is set as a threshold T. At 394, the threshold T is applied to binarize the image.
At 396, very small detected blobs are removed from the binarized image, and the remaining detected blobs are labeled as objects of interest. At 397, the edges corresponding to objects of interest are used to calculate features. Features may include size, shape, etc. Edge detection is performed on the objects of interest, and features such as size and shape in the 2D space are calculated.
At 398, features for calculation of stereo disparity between the first and second infrared sensors are stored. The features detected in 397 are stored to later apply the principles of image disparity to calculate features of the object in 3D space. At 399, the procedure 300b ends.
To summarize 388 to 396, object detection is facilitated in addition to object classification to remove unwanted objects from further processing. If a field of view is a largely cold environment, and one warm object or body without occlusion is detected in the middle of the image, then the histogram of the grey levels of this image includes two clear peaks (bimodal), with a clear valley in-between them. If the histogram is not bimodal in this manner, maybe there is no animal/human in the field of view, so the processor may do nothing further with that frame. If a valley is found in between a bimodal histogram, the valley can be used as a threshold to binarize the image, creating a white blob over a black background. Typically there is noise, giving rise to small blobs that may be discarded.
The operations outlined in
The new sensor according to embodiments of the invention may also be used in other safety applications where it is critical to know the location in space of people or appendages in order to provide an automatic stop to machinery.
It should be understood that embodiments or aspects of the present invention may be performed in hardware, firmware, or software. For example, the processes associated with performing FFTs, look-up table activities, and other activities described herein, may be performed on mobile electronics devices through use of software. The software may be any form of software that can operate in a manner consistent with the example embodiments described hereinabove. The software can be stored on any non-transient computer-readable medium, such as RAM, ROM, or any magnetic or optical media known in the art. The software can be loaded and executed by a processor to perform operations consistent with embodiments described above.
While this invention has been particularly shown and described with references to example embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.