Patent Publication Number: US-10769800-B2

Title: Moving object detection apparatus, control method for moving object detection apparatus, and non-transitory computer-readable storage medium

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates to a moving object detection apparatus, a control method for a moving object detection apparatus, and a non-transitory computer-readable storage medium. 
     Description of the Related Art 
     In recent years, processing such as image analysis for detecting and tracking an object using images captured by a surveillance camera and event analysis using the results of such image analysis have been performed. One moving object detection technology that is used in image analysis is a background subtraction method. The background subtraction method involves creating a background model from a background image in which moving objects have been removed, and detecting a moving object region (foreground region) from the difference between the background model and an input image. 
     Usually, a perfect background image in which objects have been removed is difficult to obtain with one input image, and the background image also changes due to changes in the environment. Thus, the background model is modified to follow changes in the environment, by successively updating the background model. Japanese Patent No. 5391966 (Hereinafter, Document 1) discloses a technology in which luminance values before and after a change in brightness are used to infer a background model when recreating the background image in response to the change in brightness, with respect to a region in which the background cannot be created due to being blocked by a mobile object. 
     However, in the case where there are periodical changes in brightness, for example, storing the background models before and after the changes in brightness and reusing the stored background models according to the brightness is effective from the viewpoint of calculation costs saved by being able to omit inference processing. That is, there are cases where a method that involves continually inferring and using background models such as in Document 1 becomes inefficient. In view of this, in moving object detection, a technology is desired that enables it to be reliably judged whether the background model that has been used to date needs to be regenerated, or whether a stored background model can be reused. 
     SUMMARY OF THE INVENTION 
     According to one embodiment of the present invention, it becomes possible to more reliably judge whether to regenerate a background model for moving object detection. 
     According to one aspect of the present invention, there is provided a moving object detection apparatus comprising: a generating unit configured to generate a background model based on a feature of a background region of an image that is captured by an image capturing unit; a detecting unit configured to detect a moving object region from an image based on the background model; and a determining unit configured to determine whether to cause the generating unit to newly generate a background model, based on an amount of change in the moving object region detected by the detecting unit for a first image and a second image captured at different times. 
     According to another aspect of the present invention, there is provided a control method for a moving object detection apparatus, comprising: generating a background model based on a feature of a background region of an image that is captured by an image capturing unit; detecting a moving object region from an image based on the background model; and determining whether to cause generation of a new background model in the generating be executed, based on an amount of change in the moving object region detected for a first image and a second image captured at different times. 
     Furthermore, according to another aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing a program for causing a computer to execute a control method for a moving object detection apparatus, the method comprising: generating a background model based on a feature of a background region of an image that is captured by an image capturing unit; detecting a moving object region from an image based on the background model; and determining whether to cause generation of a new background model in the generating to be executed, based on an amount of change in the moving object region detected for a first image and a second image captured at different times. 
     Further features of the present invention will become apparent from the following description of exemplary embodiments (with reference to the attached drawings). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram showing an exemplary configuration of a moving object detection system according to an embodiment. 
         FIG. 2  is a flowchart of processing that is performed by a moving object detection apparatus according to a first embodiment. 
         FIGS. 3A to 3D  are diagrams showing an exemplary processing result of moving object detection. 
         FIGS. 4A and 4B  are flowcharts of processing that is performed by a moving object detection system according to a second embodiment. 
         FIGS. 5A and 5B  are flowcharts of processing that is performed by a moving object detection system according to a third embodiment. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Note that the embodiments described below illustrate examples of specific instances in which the present invention is carried out, and are working examples of the configuration recited in the claims. 
     First Embodiment 
     An exemplary configuration of a moving object detection system according to a first embodiment will be described using the block diagram of  FIG. 1 . The moving object detection system has a moving object detection apparatus  100  and a camera  101 . The moving object detection apparatus  100  has an input unit  102 , a control unit  103 , an output unit  104 , a storage unit  105 , a background model generation unit  106 , a moving object detection unit  107 , and a comparison unit  108 . The moving object detection apparatus  100  detects a moving object using a background subtraction method from an image acquired by the camera  101 , and provides a user with a detection result. Note that since the background subtraction method is a well-known technology, detailed description thereof will be omitted. Note that the “moving object” described in the present embodiment refers to a foreground region detected with the background subtraction method, and is defined as a movable object. 
     The camera  101  captures a still image or a moving image in an orientation that is set or controlled. In the present embodiment, the camera  101  is given as having a mode (hereinafter, RGB mode) for acquiring an RGB video and a mode (hereinafter, infrared mode) for acquiring an infrared video. The camera  101  outputs captured images together with a frame number. 
     In the moving object detection apparatus  100 , the input unit  102  has the function of a camera I/F that receives input of images captured by the camera  101 . Also, the input unit  102  receives commands from a user (e.g., RGB mode/infrared mode switching command, position and orientation control command including pan/tilt/zoom setting values, etc., for the camera  101 ). The control unit  103  includes a CPU and a memory which are not shown, and controls the constituent elements of the moving object detection apparatus  100  as a result of the CPU executing programs stored in the memory. For example, the control unit  103  controls the camera  101  on the basis of commands received with the input unit  102 . The output unit  104  is connected to a display apparatus (not shown), and causes images captured with the camera  101  to be displayed on the display apparatus and displays a moving object detection result in a superimposed manner. The storage unit  105  is constituted by a hard disk, for example, and stores information that is output from the constituent elements (e.g., images captured with the camera  101 , background models generated with the background model generation unit  106 , etc.). 
     The background model generation unit  106  generates and updates background models that are required in moving object detection processing by the background subtraction method, based on features of the background region of images input using the input unit  102 . A background model here is an image feature amount for every block obtained when a captured image (hereinafter, input image) that is captured with the camera  101  and input to the moving object detection apparatus  100  via the input unit  102  is divided by a predetermined division number into blocks. In the present embodiment, averages (hereinafter, RGB color averages) of the color components of respective RGB pixels within each block are used as image feature amounts for the block. 
     Also, the background model generation unit  106  holds a learning period flag. The initial value of the learning period flag is ON. While the learning period flag is ON, the background model generation unit  106  newly generates a background model from input images, assuming that the input images do not have a moving object region. Specifically, the average value of the feature amounts of each block of a plurality of input images is saved as the feature amount of the background model corresponding to that block. That is, since feature amounts need to be collected from a plurality of input images in the case of newly generating a background model, a certain learning period is needed. 
     The learning period flag changes to OFF when a predetermined period elapses. When the learning period flag is OFF, the average of the feature amount of a block determined to be the background of an input image and the feature amount of the background model corresponding to that block is calculated, and the calculation result is saved as the feature amount of the updated background model. The background model is thus successively updated. As described above, the background model generation unit  106  newly “generates” a background model while the learning period flag is ON, and “updates” the background model while the learning period flag is OFF. Generally, the time taken to perform moving object detection after newly generating a background model is set to be shorter than the time it takes to suppress misdetection after the background is updated. This is in order to be able to start moving object detection as early as possible at startup, at the same time as reducing the influence of misdetection caused by noise as much as possible. 
     The moving object detection unit  107  detects a moving object region from an input image using a background model. The moving object detection unit  107 , first, divides the input image by a predetermined division number into blocks, and acquires the RGB color averages of the pixels within the respective block regions as feature amounts. The moving object detection unit  107  then calculates the difference between the feature amount of each block and the feature amount of the background model corresponding to the block. The moving object detection unit  107  determines blocks whose difference does not exceed a predetermined threshold to be background, and blocks whose difference exceeds the predetermined threshold to be foreground (moving object). The moving object detection unit  107  then outputs a binary moving object detection result designating background/moving object for every block. 
     The comparison unit  108  calculates the ratio of the area of the moving object region relative to the entire image for the moving object detection results of two arbitrary input images, and calculates the difference between these ratios. In the present embodiment, the difference is calculated based on the equation showing in the following equation (1). Note that, in the following equation (1), diff is the difference between the ratios of the area of the moving object regions of the two arbitrary moving object detection results, Σn is the total number of blocks, and Σf 1  and Σf 2  are the numbers of blocks determined to be a moving object in the respective moving object detection results. The comparison unit  108  compares the calculated difference between the ratios of the area of the moving object regions with a threshold (a fixed value in the present embodiment), and outputs the comparison result (greater than or equal to threshold or less than threshold). 
     
       
         
           
             
               
                 
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     Note that the abovementioned constituent elements of the moving object detection apparatus  100  are communicably connected to each other. Also, the background model generation unit  106 , the moving object detection unit  107  and the comparison unit  108  may be realized by dedicated hardware, or some or all of the functions thereof may be realized by the CPU of the control unit  103  executing programs stored in the memory. 
     Next, processing that is performed by the moving object detection apparatus  100  will be described in accordance with the flowchart of  FIG. 2 . 
     In step S 201 , the control unit  103  changes the parameters of the camera  101  in accordance with commands input with the input unit  102 . For example, in the case where a change command to change from the RGB mode to the infrared mode is issued by the input unit  102 , the control unit  103  changes the camera  101  to the infrared mode. If a change in parameters is not instructed, there is no change in the operations of the camera  101 . In step S 202 , the background model generation unit  106  determines whether the learning period flag is ON. If the learning period flag is ON, it is determined to be a background creation mode, and the processing transitions to step S 203 . On the other hand, if the learning period flag is OFF, the processing transitions to step S 204 . 
     In step S 203 , the background model generation unit  106  generates a background model from input images that are input from the camera  101 , and saves the background model to the storage unit  105 . Also, the background model generation unit  106  measures the time from when this step is initially executed with the learning period flag in an ON state, and, after a predetermined period (e.g., 10 seconds), sets the learning period flag to OFF, and ends the background creation mode. During the predetermined period, the moving object detection apparatus  100  continues to create the background model from input images, without performing moving object detection. 
     In step S 204 , the moving object detection unit  107  performs moving object detection processing, using the input image ( FIG. 3A ) that is transmitted together with a frame number from the camera  101 , and the background model that is saved to the storage unit  105 . The moving object detection result is saved to the storage unit  105  together with the frame number of the input image. An example of the processing result at this time is shown in  FIG. 3B . Thereafter, the background model generation unit  106  updates the background model in relation to the blocks determined to be background. 
     In step S 205 , the control unit  103  determines whether the camera  101  was controlled in step S 201 , that is, whether the parameters were changed. If it is determined that the parameters of the camera  101  were changed, the processing transitions to step S 206 , and if it is determined that the parameters of the camera  101  were not changed, the processing transitions to step S 209 . In steps S 206  to S 208 , the amount of change in the moving object region detected by the moving object detection unit  107  is calculated for a first image and a second image captured at different times, and it is determined whether to newly generate a background model based on the calculated amount of change. Also, this determination is executed in response to a change in parameters being input from the input unit  102  (S 205 ). 
     In step S 206 , the comparison unit  108  calculates the difference between the ratios of the area of the moving object regions of the moving object detection result of one frame previous (hereinafter, the previous frame) that is stored in the storage unit  105  and the moving object detection result of the frame that is currently being processed (hereinafter, the current frame) in accordance with the above equation (1). For example, assume that the area of the moving object region of the moving object detection result in the previous frame ( FIG. 3B ) is 0.03, and that the area of the moving object region of the moving object detection result ( FIG. 3C ) in the current frame is 0.1. In this case, the difference between the moving object regions of the moving object detection results will be as shown in  FIG. 3D , and the difference between the ratios of the area of the moving object regions of the moving object detection results is 0.07. 
     In step S 207 , the background model generation unit  106  determines whether the difference between the ratios of the area of the moving object regions calculated in step S 206  is greater than or equal to a threshold. If it is determined that the difference is greater than or equal to the threshold, the processing transitions to step S 208 , and if is determined that the difference is less than the threshold, the processing transitions to step S 209 . 
     In step S 208 , the background model generation unit  106  clears the background model in the storage unit  105  (resets the background model), and sets the learning period flag to ON. As a result, when the processing returns to step S 201 , the background model generation unit  106  regenerates the background model. It is thus determined whether to generate a background model, based on the amount of change in the moving object regions detected in a first image captured before the change in parameters and a second image captured after the change in parameters. That is, the background model generation unit  106  determines to regenerate the background model in the case where the parameters of the camera  101  have been changed and the amount of change in the detected moving object regions is greater than or equal to a threshold, and newly generates a background model. In the case where it is determined to generate a new background model (when the learning period flag changes to ON), a background model is generated using images that are input during a predetermined period (period during which the learning period flag is ON) as the background image. 
     In step S 209 , the output unit  104  outputs the moving object detection result stored in the storage unit  105  in step S 204  together with the input image. For example, the output unit  104  causes the input image to be displayed on the display apparatus, and further causes the moving object detection result to be displayed in a superimposed manner. In step S 210 , in the case where the input unit  102  has received an end command from the user, the control unit  103  ends this processing. If this is not the case, the control unit  103  increments the frame number, and returns the processing to step S 201 . 
     Variations 
     Although, in the abovementioned embodiment, the change in camera modes between the RGB mode and the infrared mode is given as a change in the image capturing parameters for determining whether to generate a background model, the present invention is, of course, not limited thereto. For example, any mode (parameter) that possibly causes a change in the image may be used, such as F value, shutter speed, ISO speed, exposure, change in focus, HDR (high dynamic range) mode or switching of filters. It is also possible to target changes in the setting values of the camera, both automatic and manual, such as changes in the white balance setting value or in the exposure setting value in response to a change in the brightness of the image capturing environment. Also, changes in the image due to changes in the pan/tilt/zoom of the camera may be targeted. 
     Also, although, in the abovementioned embodiment, a background model is given as information that is set for every block obtained when an input image is divided by a predetermined division number into blocks, the present invention is not limited thereto. For example, other small region segmentation methods such as Superpixel may be used, and a background model may, of course, be given as information that is set in pixel units. Also, although the image feature amounts that are stored are given as the RGB color averages of pixels within a block, the present invention is not limited thereto, and other feature amounts may be used, such as RGB chromatic dispersion values. 
     Also, although, in the abovementioned embodiment, the equation for calculating the difference between the ratios of the area of the moving object regions of two arbitrary frame images is defined such as equation (1), the present invention is not limited thereto. For example, a configuration may be adopted in which a bounding rectangle containing a set of blocks that are determined to be a moving object is managed as a moving object region, and the difference between ratios of the area of moving object regions is calculated using the area of the rectangle. 
     Also, although, in the abovementioned embodiment, the threshold that is for determining whether it is necessary to regenerate the background model and is compared with the difference between the ratios of the area of moving object regions is given as being fixed, the present invention is not limited thereto. For example, the threshold may vary according to the state of the previous frame, as given below.
 
Threshold value=(ratio of moving object region of previous frame)× x  (%)
 
     Also, although, in the abovementioned embodiment, determination of whether to generate a background model is performed in the case where there is a change in parameters, the present invention is not limited thereto. For example, a configuration may be adopted in which determination of whether to generate a background model (S 206  to S 208 ) is executed every predetermined time interval. In this case, in the calculation of the difference between moving object detection regions that is executed in step S 206 , the difference between the moving object region detected in step S 204  and the moving object region detected at the timing of the previous determination is calculated, for example. 
     Second Embodiment 
     In a second embodiment, operations of a moving object detection system that has a function of saving/loading a background model according to the modes of a camera  101  will be described. The configurations of the moving object detection system and a moving object detection apparatus  100  are similar to the first embodiment ( FIG. 1 ). Hereinafter, description will predominantly focus on differences from the first embodiment. Also, in the second embodiment, in order to simplify the description, control (change in parameters) on the camera  101  will only target switching between the RGB mode and the infrared mode. 
     Next, processing that is performed by the moving object detection system of the second embodiment will be described in accordance with the flowcharts of  FIGS. 4A and 4B . 
     The processing of steps S 401  to S 405  is similar to the processing of steps S 201  to S 205  of the first embodiment ( FIG. 2 ). That is, the background model generation unit  106  generates a background model from input images while the learning period flag is ON (steps S 401  to S 403 ). When the learning period flag changes to OFF, the moving object detection unit  107  performs moving object detection from the input image (step S 404 ), and thereafter determines whether the mode of the camera  101  has been switched (whether the parameters have been changed) (step S 405 ). 
     In step S 406 , the comparison unit  108  saves the background model currently stored in the storage unit  105  to a different area (storage area) of the storage unit  105  in association with the mode (image capturing parameters) before mode switching of the camera  101 . For example, in the case where this step is executed after the mode of the camera  101  is switched from the RGB mode to the infrared mode, the comparison unit  108  saves the background model that is currently stored in the storage unit  105  to another area of the storage unit  105 , as the background model of the RGB mode. The background model that is currently being used is thus saved in response to a change in parameters in association with the parameters before the change. 
     Steps S 407  and S 408  are respectively similar to steps S 206  and S 207 . That is, the comparison unit  108  calculates the difference between the ratios of the area of the moving object regions of the moving object detection result of the previous frame stored in the storage unit  105  and the moving object detection result of the current frame (step S 407 ), and determines whether the calculation result is greater than or equal to a threshold (step S 408 ). If the difference is greater than or equal to the threshold, the processing transitions to step S 409 , and, if less than the threshold, the processing transitions to step S 415 . 
     In step S 409 , the comparison unit  108  checks whether a background model associated with the mode after the change in parameters is stored in the storage unit  105 . If a background model associated with the mode after the change is stored, the processing transitions to step S 410 , and, if not stored, the processing transitions to step S 414 . For example, if a background model associated with the infrared mode is stored in the storage unit  105  in the case where this step is executed after switching from the RGB mode to the infrared mode, the processing transitions to step S 410 , and, if not stored, the processing transitions to step S 414 . 
     In steps S 410  and S 411 , the moving object detection unit  107  acquires the background model corresponding to the parameters after the change from the storage area of the storage unit  105 , and detects a moving object region from the input image. That is, in step S 410 , the comparison unit  108  loads the background model associated with the mode after the change in parameters from the storage unit  105 , and replaces the background model currently being used. In step S 411 , the moving object detection unit  107  performs moving object detection processing, using the input image from the camera  101  and the background model that replaced the background model that was being used in step S 410 . In step S 412 , the comparison unit  108  calculates the difference between the ratios of the area of the moving object regions of the moving object detection result of the previous frame stored in the storage unit  105  and the moving object detection result obtained in step S 411  in accordance with equation (1). 
     The processing of steps S 413  to S 416  is similar to the processing of steps S 207  to S 210  in  FIG. 2 . That is, the background model generation unit  106 , in the case where the difference between the ratios of the area of the moving object regions calculated in step S 412  is greater than or equal to a threshold (YES in step S 413 ), clears the background model, and sets the learning period flag to ON (step S 414 ). Thereafter, the output unit  104  outputs the moving object detection result (step S 415 ), and the control unit  103  determines whether to end the processing in accordance with a command from the user (step S 416 ). 
     Variations 
     In the abovementioned second embodiment, in the case where the amount of change in the moving object region detected by the moving object detection unit from the input image using the background model corresponding to the parameters before the change is smaller than the threshold, that background model continues to be used. That is, moving object detection is performed in response to the mode (parameters) of the camera having been changed, and in the case where the amount of change in the moving object region is greater than or equal to the threshold, moving object detection is performed with the background model corresponding to the mode after the change, and it is judged whether to regenerate the background model. However, the present invention is not limited thereto. A configuration may be adopted such that, in the case where it is detected that the mode has been changed, the current background model is immediately saved, moving object detection is performed after loading the background model corresponding to the mode after the change, and it is judged whether to regenerate the background model. In this case, in the flowcharts of  FIGS. 4A and 4B , steps S 404 , S 407  and S 408  can be omitted, and the processing can skip to step S 414  in the case of NO in step S 409 . 
     Also, although, in the abovementioned second embodiment, control on the camera  101  targeted switching between the RGB mode and the infrared mode, the present invention is not limited thereto. Similarly to the variations of the first embodiment, any mode that possibly causes a change in the image is applicable. For example, it is possible to target changes in the pan/tilt/zoom of the camera, in which case a configuration may be adopted in which a background model is stored in response to a control value on the pan/tilt/zoom of the camera. 
     Third Embodiment 
     In a third embodiment, it is judged whether to regenerate a background model using a plurality of input images. Note that the configurations of a moving object detection system and a moving object detection apparatus  100  of the third embodiment are similar to the first embodiment ( FIG. 1 ). Hereinafter, description will predominantly focus on the differences from the first and second embodiments, and the present embodiment is hereinafter assumed to be similar to the first and second embodiments unless specifically stated otherwise. 
     In the third embodiment, the storage unit  105  holds a determination count and a determination period flag. The determination count represents the number of times that the difference between the ratios of the area of the moving object regions of the moving object detection results of the previous frame and the current frame was greater than or equal to a threshold. The determination period flag is a flag that is ON during the period in which the determination of whether the difference between the ratios of the area of the moving object regions of the moving object detection results of the previous frame and the current frame is greater than or equal to the threshold is performed, and is OFF during the period in which this determination is not performed. The following condition, for example, can thereby be set: Newly generate a background model when the number of times that it is determined that the difference between the ratios of the area of the moving object regions detected from the previous frame and the current frame is greater than or equal to the threshold exceeds 4 during the period from when the parameters are changed until the 10th frame. In this case, the determination period flag changes to ON at the stage that the change in parameters occurs. In the case where the 10th frame arrives without the determination count reaching 4 or the determination count reaches 4 before the 10th frame, the determination period flag changes to OFF. 
     Next, processing that is performed by the moving object detection apparatus  100  of the third embodiment will be described in accordance with the flowcharts of  FIGS. 5A and 5B . 
     The processing of steps S 501  to S 504  is similar to the processing of steps S 201  to S 204  of the first embodiment ( FIG. 2 ). That is, the background model generation unit  106  generates a background model from input images while the learning period flag is ON (steps S 501  to S 503 ). When the learning period flag changes to OFF, the moving object detection unit  107  performs moving object detection from the input image (step S 504 ). 
     In step S 505 , the control unit  103  determines whether the camera  101  has been controlled (whether the parameters have been changed) or whether the determination period flag is ON. In the case where the camera  101  has been controlled or the determination period flag is ON, the processing transitions to step S 506 , and, in the case where the camera  101  has not been controlled and the determination period flag is OFF, the processing transitions to step S 511 . 
     The processing of step S 506  is similar to step S 206 . That is, the comparison unit  108  calculates the difference between the ratios of the area of the moving object regions of the moving object detection result of the previous frame stored in the storage unit  105  and the moving object detection result of the current frame. Note that the ratio of the moving object region detected from the image before the change in parameters is used for the moving object detection result of the previous frame. That is, in step S 506 , the moving object detection result of the latest frame after the change in parameters and the moving object detection result of the frame immediately previous to the change in parameters are used, and the difference between the ratios of the area of the moving object regions is calculated. In other words, it is determined whether to generate a new background model based on the number of images in which the amount of change in the ratios of the moving object regions is greater than or equal to the threshold, among a plurality of images obtained after the change in parameters, within a predetermined period after the change in parameters (period during which the determination period flag is ON). In the case where the difference in the ratios of the area of the moving object regions calculated in step S 506  is greater than or equal to the threshold, the processing transitions from step S 507  to step S 507   a . Also, in the case where the difference in the ratios of the area of the moving object regions calculated in step S 506  is less than the threshold, the processing transitions from step S 507  to step S 511 . In step S 507   a , in the case where the determination period flag is OFF, the control unit  103  sets the determination period flag to ON, and saves the frame number at that time to the storage unit  105 . Thereafter, the processing advances to step S 508 . 
     The background model generation unit  106  increments the determination count in step S 508 , and determines whether the determination count exceeds the threshold in step S 509 . If it is determined that the determination count exceeds the threshold, the processing transitions to step S 510 , and, if it is determined that the determination count does not exceed the threshold, the processing transitions to step S 511 . In step S 510 , the background model generation unit  106  clears the current background model, similarly to step S 208 . Also, the control unit  103  sets the determination period flag to OFF. 
     In step S 511 , the control unit  103  updates the determination period flag. If the determination period flag is ON when step S 511  is executed, the number of processed frames from when the determination period flag changed to ON (number of times that a difference occurred) is calculated, and if the number of processed frames exceeds a given number, the determination period flag is set to OFF and the determination count is set to 0. No processing is performed if the determination period flag is OFF when step S 511  is executed. 
     Steps S 512  and S 513  are similar to steps S 209  and S 210  in  FIG. 2 . That is, the moving object detection result is output and it is determined whether to end the processing in accordance with a command from the user. As described above, in the third embodiment, it is determined whether to generate a new background model, based on the number of images (determination count) in which the amount of change in the moving object region is greater than or equal to a threshold, among the images input within a predetermined period after the change in parameters (period during which the determination period flag is ON). 
     As described above, according to the above embodiments, it is judged whether the background model that has been used to date is valid based on the amount of change in the moving object region. The background model is reused if judged to be valid, and the background model is regenerated if judged to be invalid. Accordingly, the frequency with which new background models are generated decreases, and it becomes possible to reduce the learning period for background model generation. 
     Embodiment(s) of the present invention can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as a ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s). The computer may comprise one or more processors (e.g., central processing unit (CPU), micro processing unit (MPU)) and may include a network of separate computers or separate processors to read out and execute the computer executable instructions. The computer executable instructions may be provided to the computer, for example, from a network or the storage medium. The storage medium may include, for example, one or more of a hard disk, a random-access memory (RAM), a read only memory (ROM), a storage of distributed computing systems, an optical disk (such as a compact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™), a flash memory device, a memory card, and the like. 
     While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions. 
     This application claims the benefit of Japanese Patent Application No. 2017-086547, filed Apr. 25, 2017, which is hereby incorporated by reference herein in its entirety.