Patent Publication Number: US-11386576-B2

Title: Image processing apparatus, method of tracking a target object, and storage medium

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present disclosure relates to an image processing apparatus, a method of tracking a target object, and a storage medium. 
     Description of the Related Art 
     Technologies to track objects or human bodies detected from images that are captured by cameras have hitherto been proposed. Japanese Patent Laid-Open No. 2002-373332 proposes a technology to perform tracking using template matching considering how templates are overlapped with each other to estimate a search position on the subsequent frame image from a motion vector. In addition, Japanese Patent Laid-Open No. 2007-257358 proposes a technology to efficiently detect and track a concerned object using a pyramid image in order to support enlargement and reduction of the concerned object that is being tracked and using the fact that frame images that are temporally close to each other has high correlation. In other words, when the concerned object is detected in any level in the pyramid image on the preceding frame image, the object detection process is performed for the subsequent frame image on the same level as the level on which the concerned object has been detected on the preceding frame image. 
     SUMMARY OF THE INVENTION 
     According to one embodiment of the present disclosure, an image processing apparatus that tracks a target object by estimating a position of the target object on each of a plurality of images, comprises: a first estimating unit configured to perform a first estimation process to estimate the position of the target object in at least one of the plurality of images; a second estimating unit configured to perform a second estimating process to estimate the position of the target object in at least one of the plurality of images, wherein estimating accuracy of the position of the target object in the second estimating process is lower than the estimating accuracy in the first estimation process; and a correcting unit configured to, based on the position of the target object estimated by the first estimation process on a first image of the plurality of images, correct the position of the target object estimated by the second estimation process on a second image of the plurality of the images, wherein the second image is an image captured at a different time from the first image. 
     According to another embodiment of the present disclosure, a method of tracking a target object comprises: performing a first estimation process to estimate the position of the target object in at least one of the plurality of images; performing a second estimating process to estimate the position of the target object in at least one of the plurality of images, wherein estimating accuracy of the position of the target object in the second estimating process is lower than the estimating accuracy in the first estimation process; and correcting the position of the target object estimated by the second estimation process on a second image of the plurality of the images, based on the position of the target object estimated by the first estimation process on a first image of the plurality of images, wherein the second image is an image captured at a different time from the first image. 
     Further features of the present disclosure 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 illustrating an example of the configuration of an image processing apparatus of a first embodiment; 
         FIG. 2  is a flowchart illustrating an example of an image processing process in the image processing apparatus of the first embodiment; 
         FIG. 3  is a flowchart illustrating an example of a process of correcting a shift of a tracking position in the first embodiment; 
         FIG. 4  is a diagram used for description of an example of smoothing of a locus; 
         FIG. 5  is an explanatory diagram of determination of the shift of the tracking position (Case 1); 
         FIG. 6  is an explanatory diagram of determination of the shift of the tracking position (Case 2); 
         FIG. 7  is an explanatory diagram of determination of the shift of the tracking position (Case 3); 
         FIG. 8  is an explanatory diagram of correction of the shift of the tracking position (Case 1); 
         FIG. 9  is an explanatory diagram of correction of the shift of the tracking position (Case 2); 
         FIG. 10  is an explanatory diagram of correction of the shift of the tracking position (Case 3); 
         FIG. 11  is a flowchart illustrating an example of a process of correcting the shift of the tracking position in a second embodiment; 
         FIG. 12  is a flowchart illustrating an example of a process of correcting the shift of the tracking position in a third embodiment; 
         FIG. 13  is a block diagram illustrating an example of the configuration of an image processing apparatus of a fourth embodiment; 
         FIG. 14  is a flowchart illustrating an example of an image processing process in the image processing apparatus of the fourth embodiment; 
         FIG. 15  is a flowchart illustrating an example of an image processing process in the image processing apparatus of a fifth embodiment; 
         FIG. 16  is a flowchart illustrating an example of an image processing process in the image processing apparatus of a sixth embodiment; 
         FIG. 17A  to  FIG. 17C  are diagrams illustrating an example of a general process of detecting tracking target objects and a detection result in a seventh embodiment; and 
         FIG. 18A  to  FIG. 18B  are diagrams illustrating a process of detecting the tracking target objects for suppressing the shift of the tracking position and a detection result in the seventh embodiment. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     In the methods described in Japanese Patent Laid-Open No. 2002-373332 and Japanese Patent Laid-Open No. 2007-257358, the position where a concerned object appears and the size of the object are estimated on the subsequent frame image based on the moving direction, the speed, or the size of the object on the immediately preceding frame image to set a search area based on the estimation. The search for the concerned object only in the search area not only reduces the processing cost but also inhibits an object other than the concerned object that should be tracked from being erroneously recognized as a tracking target. However, in the object detection process, a tracking position indicating the position where the object, which is the tracking target, is detected may be shifted generally due to the influence of a peripheral similar pattern including noise and background or an adjacent object. 
     In order to resolve the above issue, according to an embodiment of the present disclosure, it is possible to suppress the shift of the tracking position of an object in an image in tracking of the object. 
     Embodiments of the present disclosure will herein be described in detail with reference to the drawings. The configurations indicated in the embodiments described below are only examples and the present disclosure is not limited to the configurations illustrated in the drawings. 
     An image processing apparatus according to an embodiment detects the concerned object across the images of multiple frames to track the concerned object. In the object detection process to detect an object, the tracking position of the object to be tracked may be slightly shifted generally due to the influence of a peripheral similar pattern including noise and background or an adjacent object. This slight shift of the tracking position may become an issue. For example, in counting of the number of human bodies passing through a certain line in an image, such a slight shift of the tracking position may become an issue. Here, the passing line is set in the horizontal direction in a central portion of the screen for description. It is assumed that the number of human bodies passing through the passing line from the top to the bottom is counted as In and the number of human bodies passing through the passing line from the bottom to the top is counted as Out. A case will now be considered in which a slight shift occurs near the passing line during tracking of a human body moving from the top to the bottom. When the human body passes through the passing line from the bottom to the top due to the slight shift occurring in a direction opposite to the movement direction on the subsequent frame image although the human body is counted as In on the preceding frame image, the count of Out is incremented by one on this frame image. In addition, since the shift is corrected on the subsequent frame and the human body passes through the passing line from the top to the bottom again, the count of In is incremented by one. As a result, one error count of In and one error count of Out occur. Accordingly, with the image processing apparatus according to the present embodiment, it is possible to suppress the shift of the tracking position of an object in an image to suppress an occurrence of the error count in the counting of the number of human bodies passing through the certain line. 
     In addition, in the tracking of the concerned object in an image, smoothing of a locus may be performed in order to reduce the influence of the slight shift of the tracking position included in the locus of the concerned object to be tracked. Also in the counting of the number of human bodies passing through the certain line, the error count is capable of being suppressed by smoothing the locus before the tracking position is determined to correct the tracking position. However, minute information about the locus is lost with the method of evenly smoothing the locus. As a result, for example, determination of a suspicious behavior such as wandering of a human body is disabled to cause a problem. Accordingly, with the image processing apparatus according to the present embodiment, it is possible to suppress the shift of the tracking position while suppressing the loss of the minute information about the locus. The image processing apparatus according to the present embodiment will be described. 
     The image processing apparatus according to the present embodiment has a function to correct the shift of the tracking position of the concerned object using past locus information that is recorded. Use of the locus information enables the positions on the frame images of the concerned object to be tracked (hereinafter referred to as a tracking target object) to be identified for the respective frames in time series order. The locus information represents a tracking history of each frame when the tracking target object has been tracked. The locus information includes, for example, an object identifier (ID) (object identification information) for identifying each object on the frame image, the position of the object on the frame image (the object position), and temporal information about each frame. The locus information may include the size of the tracking target object (the size on the frame image), attribute information about the object, and so on. Although the human body is exemplified as the tracking target object in the following description, the tracking target object is not limited to the human body and may be an arbitrary object. 
     First Embodiment 
       FIG. 1  is a block diagram illustrating an example of the configuration of an image processing apparatus  100  of a first embodiment. 
     The image processing apparatus  100  of the first embodiment includes an image acquiring unit  101 , a tracking unit  102 , a locus analyzing unit  103 , a locus smoothing unit  104 , a tracking position correcting unit  105 , an output unit  106 , and a storage device  107 . 
       FIG. 2  is a flowchart illustrating an example of an image processing process in the image processing apparatus  100  of the first embodiment.  FIG. 2  illustrates a processing flow from detection and tracking of an object (human body) to correction of the shift of the tracking position. 
     Referring to  FIG. 2 , in Step S 201 , the image acquiring unit  101  acquires images to be processed in time series order from an external apparatus, such as a camera or a server. In other words, the image acquiring unit  101  acquires images captured by the camera. For example, the respective frame images composing a moving picture are supposed as the images in time series order. The image acquiring unit  101  may acquire the images in time series order stored in an external memory or the like. In Step S 202 , the image processing apparatus  100  determines whether the image acquisition succeeded. When the image acquisition by the image acquiring unit  101  failed or the image acquisition is stopped by a user, the image processing apparatus  100  determines that the image acquisition failed. If the image processing apparatus  100  determines that the image acquisition failed (NO in Step S 202 ), the process in the flowchart in  FIG. 2  is terminated. 
     If the image processing apparatus  100  determines that the image acquisition succeeded (YES in Step S 202 ), in Step S 203 , the tracking unit  102  detects the tracking target object (the concerned object) from an image of a concerned frame (the current frame) acquired by the image acquiring unit  101 . It is assumed in the first embodiment that n-number objects are detected (n-number human bodies are detected). In addition, a background subtraction method is used as the method of detecting an object in the first embodiment. The object to be detected here is, for example, a moving object or a foreground detected using the background subtraction method. Alternatively, the object to be detected may be a portion that is determined not to be the background. The information about the object detected by the tracking unit  102  includes the position on a concerned frame image, a circumscribed rectangle of the detected object, and the size of the circumscribed rectangle. In Step S 204 , the tracking unit  102  initializes a variable i. The tracking unit  102  manages each object detected in Step S 203  using the variable i initialized in Step S 204 . 
     In Step S 205 , the tracking unit  102  determines whether the value of the variable i is lower than N. If the tracking unit  102  determines that the value of the variable i is lower than N (YES in Step S 205 ), the process goes to Step S 206 . If the tracking unit  102  determines that the value of the variable i is not lower than N (NO in Step S 205 ), the process goes to Step S 208 . 
     In Step S 208 , the tracking unit  102  outputs the tracking result. Information about the tracking result is stored (recorded) in the storage device  107 . Then, the process goes back to Step S 201 . 
     If the value of the variable i is lower than N (YES in S 205 ), in Step S 206 , the tracking unit  102  detects a human body from a local area where the object of the variable i is detected and associates the detected object with the detected human body to determine a tracking area of the variable i. The human body of the variable i is hereinafter referred to as a human body i and the tracking area of the variable i is hereinafter referred to as a tracking area i. In the first embodiment, the detection of the human body is performed using a pattern matching process. The tracking unit  102  newly adds a human body ID to the human body who newly appears. When the association between the human body detected from the concerned frame image and the human body that is detected on the preceding frame image of the concerned frame in time series order succeeded, the tracking unit  102  adds the human body ID added on the preceding frame image is added also to the human body detected from the concerned frame image. The tracking unit  102  associates the human body detected from the concerned frame image with the human body detected on the preceding frame image in the above manner to perform the tracking. 
     Two processing methods: geometric pattern matching and color pattern matching are used in the pattern matching process used as the human body detection method in the first embodiment. Known methods are available for the geometric pattern matching and the color pattern matching. For example, a process disclosed in Kunimitsu, Asama, Kawabata, &amp; Mishima (2004) “Detection of Object under Outdoor Environment with Binary Edge Image for Template” IEEJ Trans, EIS, Vol. 124, No. 2 may be used as the geometric pattern matching. For example, a method of finding the correlation between color histograms in the rectangle of a tracking frame corresponding to a human body may be used as the color pattern matching. 
     The tracking unit  102  of the first embodiment sets the tracking area of the human body when the association succeeded after the human body is detected using the geometric pattern matching as the human body detection method as a high-reliability tracking area. Since it is considered that the human body to be tracked is likely to exist in the tracking area of the human body for which the association succeeded using the geometric pattern matching, the tracking area of the human body for which the association succeeded using the geometric pattern matching is set as the high-reliability tracking area indicating a high reliability. In addition, the tracking unit  102  sets the tracking area of the human body when the association succeeded after the human body is detected using the color pattern matching as the human body detection method as a medium-reliability tracking area. Since the human body to be tracked is less likely to exist in the tracking area of the human body for which the association succeeded using the color pattern matching, compared with that in the geometric pattern matching, the tracking area of the human body for which the association succeeded using the color pattern matching is set as the medium-reliability tracking area indicating a medium reliability. The tracking unit  102  determines the human body ID of the human body for which the association failed by calculating the tracking position on the concerned frame image (the current frame image) using an average velocity vector calculated from the locus to the preceding frame in time series order. The tracking unit  102  sets the tracking area detected from the concerned frame image when the association failed as a low-reliability tracking area. Since the human body to be tracked is less likely to exist in the tracking area detected from the concerned frame image using the average velocity vector, compared with that in the color pattern matching, the tracking area detected from the concerned frame image using the average velocity vector is set as the low-reliability tracking area indicating a low reliability. 
     Although the example is described in the first embodiment in which an object is detected using the background subtraction method, the method of detecting an object is not limited to the background subtraction method and another method may be used as long as an object is capable of being detected from an image using the method. The method of detecting a human body from an image is not limited to the pattern matching process and another method may be used as long as a human body is capable of being detected from an image using the method. In addition, the detection of a human body is not limitedly performed in the local area where the object is detected and the detection of a human body may be performed in the entire image. Furthermore, the detection target object is not limited to the human body and may be any object capable of being detected as a specific object (for example, an object having a specific feature or an object determined to have a specific pattern). For example, the detection target object may be an automobile or an animal. 
     After Step S 206 , the process goes to Step S 300  to perform correction of the shift of the tracking position. The correction of the shift of the tracking position in Step S 300  is performed by the locus analyzing unit  103 , the locus smoothing unit  104 , and the tracking position correcting unit  105 . After Step S 300 , the process goes to Step S 207 . In Step S 207 , the tracking unit  102  increments the variable i. Then, the process goes back to Step S 205 . 
       FIG. 3  is a flowchart illustrating an example of a detailed process of correcting the shift of the tracking position in Step S 300 . 
     Referring to  FIG. 3 , in Step S 301 , the locus analyzing unit  103  estimates a moving speed of the human body, which has been detected in the above manner. The estimation of the moving speed is performed using only the high-reliability tracking areas in the locus in the first embodiment. Here, for description, it is assumed that the human body i is being tracked. The high-reliability tracking area corresponding to the human body i is denoted by R i (k) and the high-reliability tracking area temporally adjacent to the high-reliability tracking area R i (k) is denoted by R i (k+1). The central coordinate of the high-reliability tracking area R i (k) is denoted by (R i   x (k), R i   y (k)) and the central coordinate of the high-reliability tracking area R i (k+1) is denoted by (R i   x (k+1), R i   y (k+1)). In the representations, k=(1, 2, . . . , K) and k is the ID added to the frame image to which the high-reliability tracking area in the locus of the human body i belongs. The frame images are arranged in order of time and the IDs are added to the frame images in chronological order of their timestamps. The velocity vector of the k-th high-reliability tracking area R i (k) is denoted by Equation (1). In Equation (1), Δt denotes the time interval between the timestamp of the frame image to which the high-reliability tracking area R i (k) belongs and the timestamp of the frame image to which the high-reliability tracking area R i (k+1) belongs. 
     
       
         
           
             
               
                 
                   
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     In Step S 302 , the locus analyzing unit  103  calculates the reliability of the tracking history. In Step S 303 , the locus analyzing unit  103  determines the reliability of the tracking history based on the reliability of each tracking position composing the tracking history. The reliability of each tracking position is the reliability of the tracking area. The tracking area is classified into the high-reliability tracking area, the medium-reliability tracking area, and the low-reliability tracking area, described above, in the first embodiment. In addition, the reliabilities of the tracking areas are determined based on the detection method (the geometric pattern matching or the color pattern matching) used in the detection of the human body (that is, the detection of the position of the tracking area), as described above. In other words, the locus analyzing unit  103  determines the reliability of the tracking history based on the detection method when the respective tracking positions of the human body, which compose the tracking history of the human body to be tracked, are detected. 
     Specifically, the locus analyzing unit  103  classifies the reliability of the tracking history of the human body i that is being tracked into a high-reliability locus, a medium-reliability locus, or a low-reliability locus based on the ratio of the number of the high-reliability tracking areas (the number of the tracking positions having a certain reliability) to the number of the tracking areas in the locus. For example, when the total number of the tracking areas composing the locus is 100 and the number of the high-reliability tracking areas is 70, the ratio of the number of the high-reliability tracking areas is 0.7. 
     The locus analyzing unit  103  determines whether the locus is the high-reliability locus based on the ratio of the number of the high-reliability tracking areas to the number of the tracking areas in the locus in Step S 303 . In the first embodiment, the locus analyzing unit  103  determines that the locus is the high-reliability locus if a condition is met in which the ratio of the number of the high-reliability tracking areas to the number of the tracking areas in the locus is higher than or equal to a high-reliability determination threshold value. If the locus analyzing unit  103  determines that the locus is determined to be the high-reliability locus (YES in Step S 303 ), the process goes to Step S 304 . If the locus analyzing unit  103  determines that the locus is not the high-reliability locus (NO in Step S 303 ), the process goes to Step S 310 . 
     In Step S 304 , the locus smoothing unit  104  performs locus smoothing to the high-reliability locus to create a smoothing tracking history. The locus smoothing for the high-reliability locus is varied depending on the reliabilities of the tracking areas in the locus and the positional relationship between the tracking areas. 
     The locus smoothing unit  104  does not perform the smoothing to the high-reliability tracking area included in the high-reliability locus. In contrast, the locus smoothing unit  104  performs the smoothing to the medium-reliability tracking area and the low-reliability tracking area included in the high-reliability locus in different manners for different four types: from Type 1 to Type 4 illustrated in  FIG. 4 . The tracking areas are classified into the four types based on their reliabilities and the positional relationship between the tracking areas. Each white circle in  FIG. 4  indicates the tracking area of the human body detected from the local area including the object (local detection (LD)). Such a tracking area is referred to as an LD tracking area, which is the high-reliability tracking area. Each black circle in  FIG. 4  indicates the tracking area of the human body detected from the frame image. Such a tracking area is referred to as a non-LD tracking area, which is the medium-reliability tracking area or the low-reliability tracking area. The classification into the four types: Type 1 to Type 4 is performed to the tracking area (the non-LD tracking area) indicated by the black circle in  FIG. 4 , which is the medium-reliability tracking area or the low-reliability tracking area. Each hatched circle in  FIG. 4  indicates the tracking area after the locus smoothing (a smoothed tracking area). 
     In the first embodiment, the tracking area that is detected from the concerned frame image and that is classified into Type 1 is a tracking area that is not a high-reliability tracking area on the frame image immediately before the concerned frame image and is a high-reliability tracking area on the frame image immediately after the concerned frame image. The tracking area that is classified into Type 2 is a tracking area that is a high-reliability tracking area on the frame image immediately before the concerned frame image and is not a high-reliability tracking area on the frame image immediately after the concerned frame image. The tracking area that is classified into Type 3 is a tracking area that is a high-reliability tracking area on the frame images immediately before and immediately after the concerned frame image. The tracking area that is classified into Type 4 is a tracking area that is not a high-reliability tracking area on the frame images immediately before and immediately after the concerned frame image. 
     How the tracking areas of Type 1 to Type 4 are smoothed by the locus smoothing unit  104  of the first embodiment will now be described with reference to  FIG. 4 . 
     In the smoothing of a tracking area  401  of Type 1, the locus smoothing unit  104  uses a high-reliability tracking area  403  on the frame image immediately after the concerned frame as a reference tracking area. The locus smoothing unit  104  moves the tracking area  401  of Type 1 to a position calculated based on the average velocity vector calculated in Step S 301  and the time between the concerned frame (frame 0) and the immediately subsequent frame (frame 1). At this time, the locus smoothing unit  104  identifies a correction line segment, for example, which is parallel to the direction indicated by the average velocity vector and that passes through the position of the high-reliability tracking area  403 . In addition, the locus smoothing unit  104  multiplies the speed indicated by the average velocity vector by the time between frame 0 and frame 1 to calculate the distance. Then, the locus smoothing unit  104  corrects the position of the tracking area  401  to a position on the identified correction line segment, which is apart from the position of the high-reliability tracking area  403  in a direction opposite to that of the average velocity vector by the calculated distance. In the example in  FIG. 4 , the tracking area to which the tracking area  401  of Type 1 is moved in the smoothing of the tracking area  401  of Type 1 is a smoothed tracking area  402 . As described above, in the smoothing of the tracking area of Type 1, the locus smoothing unit  104  corrects a first tracking position (a first tracking area) of an object estimated in a first image in the following manner. Specifically, the locus smoothing unit  104  corrects the first tracking position of the object on the first image based on a second tracking position (a second tracking area) of the object on a second image, which is the subsequent frame of the first image in time series. The second tracking position has a reliability higher than that of the first tracking position. 
     In the smoothing of a tracking area  408  of Type 2, the locus smoothing unit  104  uses a high-reliability tracking area  406  on the frame image (frame 3) immediately before the concerned frame (frame 4) as the reference tracking area. The locus smoothing unit  104  moves the tracking area  408  of Type 2 to a position calculated based on the average velocity vector calculated in Step S 301  and the time between the concerned frame (frame 4) and the immediately preceding frame (frame 3). At this time, the locus smoothing unit  104  identifies the correction line segment, for example, which is parallel to the direction indicated by the average velocity vector and that passes through the position of the high-reliability tracking area  406 . In addition, the locus smoothing unit  104  multiplies the speed indicated by the average velocity vector by the time between frame 3 and frame 4 to calculate the distance. Then, the locus smoothing unit  104  corrects the position of the tracking area  408  to a position on the identified correction line segment, which is apart from the position of the high-reliability tracking area  406  in the direction indicated by the average velocity vector by the calculated distance. In the example in  FIG. 4 , the tracking area to which the tracking area  408  of Type 2 is moved in the smoothing of the tracking area  408  of Type 2 is a smoothed tracking area  409 . As described above, in the smoothing of the tracking area of Type 2, the locus smoothing unit  104  corrects the first tracking position (the first tracking area) of an object estimated on the first image in the following manner. Specifically, the locus smoothing unit  104  corrects the first tracking position of the object on the first image based on the second tracking position (the second tracking area) of the object on the second image, which is the preceding frame of the first image in time series. The second tracking position has a reliability higher than that of the first tracking position. As described above in Type 1 to Type 2, the locus smoothing unit  104  corrects the first tracking position of the object on the first image based on the second tracking position of the object on the second image, which is captured at a time different from that of the first image. The second tracking position has a reliability higher than that of the first tracking position. 
     In the smoothing of a tracking area  404  of Type 3, the locus smoothing unit  104  determines a line segment  405  both ends of which are the high-reliability tracking areas  403  and  406  on the frame images immediately before and immediately after the concerned frame. Next, the locus smoothing unit  104  calculates a position on the line segment  405 , which is distributed at a ratio corresponding to the time between the concerned frame to which the tracking area  404  of Type 3 belongs and the immediately preceding frame and the time between the concerned frame and the immediately subsequent frame. Then, the locus smoothing unit  104  moves the tracking area  404  of Type 3 to the position distributed on the line segment  405 . In the example in  FIG. 4 , the tracking area to which the tracking area  404  of Type 3 is moved in the smoothing of the tracking area  404  of Type 3 is a smoothed tracking area  407 . As described above, in the smoothing of the tracking area of Type 3, the locus smoothing unit  104  corrects the first tracking position (the first tracking area) of an object estimated on the first image in the following manner. Specifically, the locus smoothing unit  104  corrects the first tracking position of the object on the first image based on each of the tracking positions of the object on the second image, which is the preceding frame of the first image in time series, and a third image, which is the subsequent frame of the first image in time series. The tracking positions have reliabilities higher than that of the first tracking position. 
     The tracking area after the medium-reliability tracking area or the low-reliability tracking area is smoothed in the above manner is subsequently processed as the high-reliability tracking area. Upon termination of the smoothing of the high-reliability locus described above, the process goes to Step S 305 . Step S 305  and the subsequent steps are described below. 
     If the locus analyzing unit  103  determines that the locus does not meet the condition of the high-reliability locus (NO in Step S 303 ), in Step S 310 , the locus analyzing unit  103  determines whether the locus is the medium-reliability locus. The locus analyzing unit  103  determines that the locus is the medium-reliability locus when a condition is met in which the ratio of the number of the high-reliability tracking areas to the number of the tracking areas in the locus is, for example, higher than or equal to a medium-reliability determination threshold value (higher than or equal to the medium-reliability determination threshold value and lower than the high-reliability determination threshold value). If the locus analyzing unit  103  determines that the locus is the medium-reliability locus (YES in Step S 310 ), the process goes to Step S 311 , which is performed by the locus smoothing unit  104 . If the locus analyzing unit  103  determines that the locus is not the medium-reliability locus (NO in Step S 310 ), that is, if the locus analyzing unit  103  determines that the locus is the low-reliability locus, the correction of the shift of the tracking position in Step S 300  is terminated and the process goes to Step S 207  in  FIG. 2 . 
     In Step S 311 , the locus smoothing unit  104  performs the locus smoothing to the medium-reliability locus. The locus smoothing of the medium-reliability locus is performed to only the tracking areas classified into Type 1 and Type 2 described above, among the tracking areas included in the medium-reliability locus. The tracking area smoothed in the locus smoothing of the medium-reliability locus is subsequently processed as the high-reliability tracking area, as in the locus smoothing of the high-reliability locus in Step S 304 . 
     In Step S 312 , the locus smoothing unit  104  re-estimates the moving speed estimated in Step S 301 . The estimation method in Step S 312  is the same as the method of estimating the moving speed in Step S 301 . 
     In Step S 313 , the locus smoothing unit  104  re-calculates the reliability of the locus subjected to the locus smoothing in Step S 311  (the reliability of the tracking locus). 
     In Step S 314 , the locus smoothing unit  104  determines whether the locus is the high-reliability locus (that is, the locus is made the high-reliability locus in the locus smoothing in Step S 311 ). If the locus smoothing unit  104  determines that the locus is the high-reliability locus (YES in Step S 314 ), the process goes back to Step S 305 , which is performed by the tracking position correcting unit  105 . If the locus smoothing unit  104  determines that the locus is not the high-reliability locus (NO in Step S 314 ), the correction of the shift of the tracking position in Step S 300  is terminated and the process goes to Step S 207  in  FIG. 2 . 
     In Step S 305 , the tracking position correcting unit  105  measures the change in the moving direction in the smoothing tracking history created by the locus smoothing unit  104 . In Step S 306 , the tracking position correcting unit  105  determines the degree of stability of the locus based on the change in the moving direction. The degree of stability of the locus indicates the degree of change in the moving direction of the object to be tracked. The degree of change in the moving direction of the object to be tracked is decreased as the degree of stability is increased, and the degree of change in the moving direction of the object to be tracked is increased as the degree of stability is decreased. Specifically, in Step S 305 , the tracking position correcting unit  105  calculates the percentage of movement in the positive direction and the percentage of movement in the negative direction for the x direction and the y direction in order to digitize the degree of change in the moving direction in the locus. The calculation of the percentages of movement is performed using the average velocity vector calculated in Step S 301 . For example, when the number of the high-reliability tracking areas included in the locus is 70 and the number of the velocity vectors each having an element in the positive direction in the x direction is seven, the percentage of movement in the positive direction in the x direction is 10% and the percentage of movement in the negative direction in the x direction is 90%. 
     In Step S 306 , the tracking position correcting unit  105  determines whether the moving direction is changed in the locus. In the first embodiment, the tracking position correcting unit  105  determines that the moving direction is not changed if the percentage of movement in the positive direction or the negative direction is higher than or equal to a predetermined value in each of the x direction and the y direction. In other words, the tracking position correcting unit  105  determines that the degree of stability of the moving direction of the object is higher than a predetermined threshold value because the moving direction of the object to be tracked is stable. For example, when the predetermined value is 80%, the number of the high-reliability tracking areas is 70, and 63 velocity vectors each having an element in the positive direction in the x direction exist, the percentage of movement in the positive direction is 90%. Accordingly, the tracking position correcting unit  105  determines that “the moving direction is not changed in the x direction.” Here, the remaining seven velocity vectors each have an element in the negative direction or each have a value indicating a static state in the x direction. The tracking position correcting unit  105  determines whether the moving direction is changed in the locus also in the y direction. If the tracking position correcting unit  105  determines that the moving direction is changed in both the x direction and the y direction (YES in Step S 306 ), the correction of the shift of the tracking position in Step S 300  is terminated and the process goes to Step S 207  in  FIG. 2 . If the tracking position correcting unit  105  determines that the moving direction is not changed in at least one of the x direction and the y direction (NO in Step S 306 ), that is, if the degree of stability is higher than the predetermined threshold value in at least one of the x direction and the y direction, the process goes to Step S 307 . 
     In Step S 307 , the tracking position correcting unit  105  determines the shift of the tracking position on the last frame image that is currently being processed. Although the definition of the shift of the tracking position and the method of determining the shift of the tracking position are capable of arbitrarily determined depending on a use case or the like, a case is defined as the shift of the tracking position in the first embodiment in which movement in a direction opposite to the average movement direction calculated from the locus occurs. 
     The method of determining the shift of the tracking position based on the definition of the shift of the tracking position will now be described. 
     In the first embodiment, the tracking position correcting unit  105  separately determines the shift of the tracking position in the following three cases: Case 1 to Case 3. Case 1 of the shift of the tracking position is a case in which the moving direction is not changed in both the x direction and the y direction. Case 2 of the shift of the tracking position is a case in which the moving direction is not changed only in the y direction (the moving direction is changed in the x direction). Case 3 of the shift of the tracking position is a case in which the moving direction is not changed only in the x direction (the moving direction is changed in the y direction). 
     First, Case 1 of the shift of the tracking position, in which the moving direction is not changed in both the x direction and the y direction, will be described with reference to  FIG. 5 . First, the tracking position correcting unit  105  calculates the center point of a tracking area  502  on the preceding frame image of a tracking area  501  (the tracking area i) focused on the concerned frame image. Next, the tracking position correcting unit  105  sets a tracking position shift determination line  500 . The tracking position shift determination line  500  in Case 1 is a straight line that passes through the center point calculated for the tracking area  502  and that is orthogonal to the average velocity vector estimated in Step S 301  or Step S 312 . In the first embodiment, the area in the positive direction of the average velocity vector with respect to the tracking position shift determination line  500  is referred to as a first area for convenience and the area in the negative direction of the average velocity vector with respect to the tracking position shift determination line  500  is referred to as a second area for convenience. If the center point of the tracking area  501  (the tracking area i) is included in the second area, the tracking position correcting unit  105  determines that the shift of the tracking position occurs in the tracking area  501  (the tracking area i). 
     Next, Case 2 of the shift of the tracking position, in which the moving direction is not changed only in the y direction, will be described with reference to  FIG. 6 . First, the tracking position correcting unit  105  calculates the center point of a tracking area  602  on the preceding frame image of a tracking area  601  (the tracking area i) focused on the concerned frame image. Next, the tracking position correcting unit  105  sets a tracking position shift determination line  600 . The tracking position shift determination line  600  in Case 2 is a straight line that passes through the center point calculated for the tracking area  602  and that is parallel to the x direction. In the first embodiment, the area in the positive direction of the y-direction element of the average velocity vector with respect to the tracking position shift determination line  600  is referred to as the first area for convenience and the area in the negative direction of the y-direction element of the average velocity vector with respect to the tracking position shift determination line  600  is referred to as the second area for convenience. If the center point of the tracking area  601  (the tracking area i) is included in the second area, the tracking position correcting unit  105  determines that the shift of the tracking position occurs in the tracking area  601  (the tracking area i). 
     Next, Case 3 of the shift of the tracking position, in which the moving direction is not changed only in the x direction, will be described with reference to  FIG. 7 . First, the tracking position correcting unit  105  calculates the center point of a tracking area  702  on the preceding frame image of a tracking area  701  (the tracking area i) focused on the concerned frame image. Next, the tracking position correcting unit  105  sets a tracking position shift determination line  700 . The tracking position shift determination line  700  in Case 3 is a straight line that passes through the center point calculated for the tracking area  702  and that is parallel to the y direction. In the first embodiment, the area in the positive direction of the x-direction element of the average velocity vector with respect to the tracking position shift determination line  700  is referred to as the first area for convenience and the area in the negative direction of the x-direction element of the average velocity vector with respect to the tracking position shift determination line  700  is referred to as the second area for convenience. If the center point of the tracking area  701  (the tracking area i) is included in the second area, the tracking position correcting unit  105  determines that the shift of the tracking position occurs in the tracking area  701  (the tracking area i). 
     After the determination of the shift of the tracking position in Step S 307 , in Step S 308 , the tracking position correcting unit  105  branches the process depending on whether the shift of the tracking position occurs. If the tracking position correcting unit  105  determines that the shift of the tracking position occurred (YES in Step S 308 ), the process goes to Step S 309 . If the tracking position correcting unit  105  determines that the shift of the tracking position did not occur (NO in Step S 308 ), the correction of the shift of the tracking position in Step S 300  is terminated. 
     In Step S 309 , the tracking position correcting unit  105  corrects the tracking position on the last frame image that is currently being processed. Although the method of correcting the tracking position is capable of arbitrarily determined depending on the use case or the like, the tracking position is corrected in the following different manners for the different cases of the shift of the tracking position, described above, in the first embodiment. 
     The correction of the tracking position in Case 1 of the shift of the tracking position, in which the moving direction is not changed in both the x direction and the y direction, will be described with reference to  FIG. 8 . The same reference numerals are used in  FIG. 8  to identify the same components in  FIG. 5 . First, the tracking position correcting unit  105  calculates an estimated moving position (an estimated tracking area  803 ) of the tracking area  501  on the concerned frame image using the center point of the tracking area  502  and the average velocity vector on the preceding frame image of the concerned frame. Next, the tracking position correcting unit  105  sets a straight line  804  connecting the center point of the tracking area  501  on the concerned frame to the center point of the estimated tracking area  803  on the concerned frame and calculates the intersection between the straight line  804  and the tracking position shift determination line  500 . Then, the tracking position correcting unit  105  sets a point moved from the intersection on the straight line  804  in the direction toward the first area by several pixels as the position (the center point) of a corrected tracking area  805 . 
     Next, the correction of the tracking position in Case 2 of the shift of the tracking position, in which the moving direction is not changed only in the y direction, will be described with reference to  FIG. 9 . The same reference numerals are used in  FIG. 9  to identify the same components in  FIG. 6 . First, the tracking position correcting unit  105  calculates the estimated moving position (an estimated tracking area  903 ) of the tracking area  601  on the concerned frame image using the center point of the tracking area  602  and the y component of the average velocity vector on the preceding frame image of the concerned frame. Next, the tracking position correcting unit  105  sets a straight line  904  connecting the center point of the tracking area  601  to the center point of the estimated tracking area  903  on the concerned frame and calculates the intersection between the straight line  904  and the tracking position shift determination line  600 . Then, the tracking position correcting unit  105  sets a point moved from the intersection on the straight line  904  in the direction toward the first area by several pixels as the position (the center point) of a corrected tracking area  905 . 
     Next, the correction of the tracking position in Case 3 of the shift of the tracking position, in which the moving direction is not changed only in the x direction, will be described with reference to  FIG. 10 . The same reference numerals are used in  FIG. 10  to identify the same components in  FIG. 7 . First, the tracking position correcting unit  105  calculates the estimated moving position (an estimated tracking area  1003 ) of the tracking area  701  on the concerned frame image using the center point of the tracking area  702  and the x component of the average velocity vector on the preceding frame image of the concerned frame. Next, the tracking position correcting unit  105  sets a straight line  1004  connecting the center point of the tracking area  701  to the center point of the estimated tracking area  1003  on the concerned frame and calculates the intersection between the straight line  1004  and the tracking position shift determination line  700 . Then, the tracking position correcting unit  105  sets a point moved from the intersection on the straight line  1004  in the direction toward the first area by several pixels as the position (the center point) of a corrected tracking area  1005 . 
     After the correction of the tracking position in Step S 309 , the correction of the shift of the tracking position in Step S 300  is terminated and the process goes to Step S 207  in  FIG. 2   
     As described above, the image processing apparatus  100  of the first embodiment calculates the reliability of the tracking history of the tracking target object (a tracking target human body) and smoothes the tracking history based on the reliability of the tracking history to correct the shift of the tracking position. Consequently, according to the first embodiment, it is possible to correct the shift of the tracking position without the loss of the minute information about the locus in the detection and tracking of the human body in the image. 
     The image processing apparatus  100  of the first embodiment may have a configuration in which, if the shift of the tracking position occurs, the output from the output unit  106  is changed so that the user is capable of knowing the area where the shift of the tracking position occurs to present the change to the user. For example, when the tracking frame of the human body is output on the screen of a display device (not illustrated), the output unit  106  may change the color of the tracking frame to a certain color or may adjust the width of the frame line for the area where the shift of the tracking position occurs to present the occurrence of the shift of the tracking position to the user. Alternatively, if the shift of the tracking position occurs, the output unit  106  may continue displaying of the change of the output for a predetermined time. In this case, the output unit  106  may stop the presentation after a predetermined time elapsed. 
     Second Embodiment 
     A second embodiment will now be described. 
     The example is described above in the first embodiment in which the correction of the shift of the tracking position is performed without any condition. In the second embodiment, a method of correcting the shift of the tracking position only when a predetermined condition is met will be described. A case will be described here in which the predetermined condition is “the number of detected human bodies is smaller than a predetermined number.” 
     The configuration of the image processing apparatus  100  of the second embodiment is the same as that in  FIG. 1  described above. 
       FIG. 11  is a flowchart illustrating an example of the process of correcting the shift of the tracking position in Step S 300  in the second embodiment. The following description focuses on the difference from the first embodiment for simplicity. The same reference numerals are used in  FIG. 11  to identify the steps in which the same processing as in the steps used in the first embodiment is performed. A description of such steps is omitted herein. 
     Referring to  FIG. 11 , in Step S 1101 , the tracking position correcting unit  105  determines whether a number-of-detected-human-bodies N is lower than a predetermined threshold value (Th). If the tracking position correcting unit  105  determines that the number-of-detected-human-bodies N is lower than the predetermined threshold value Th (YES in Step S 1101 ), the process goes to Step S 301 . If the tracking position correcting unit  105  determines that the number-of-detected-human-bodies N is not lower than the predetermined threshold value Th (NO in Step S 1101 ), the correction of the shift of the tracking position in Step S 300  is terminated. 
     As described above, the image processing apparatus  100  of the second embodiment performs the correction of the shift of the tracking position only if the number of detected human bodies is smaller than a predetermined number (lower than a threshold value). Consequently, according to the second embodiment, it is possible to track the human bodies without increasing the calculation load while keeping the accuracy in the case of the tracking of human bodies of a small number in which the shift of the tracking position is relatively difficult to occur. 
     Third Embodiment 
     A third embodiment will now be described. 
     A method of correcting the shift of the tracking position only when a predetermined condition is met will be described in the third embodiment, as in the second embodiment. A case will be described here in which the predetermined condition is “the color or the texture of the tracking target human body is not similar to that of a neighboring area.” 
     The configuration of the image processing apparatus  100  of the third embodiment is the same as that in  FIG. 1  described above. 
       FIG. 12  is a flowchart illustrating an example of the process of correcting the shift of the tracking position in Step S 300  in the third embodiment. The following description focuses on the difference from the first embodiment for simplicity. The same reference numerals are used in  FIG. 12  to identify the steps in which the same processing as in the steps used in the above embodiments is performed. A description of such steps is omitted herein. 
     Referring to  FIG. 12 , in Step S 1201 , the tracking position correcting unit  105  calculates the degree of similarity between the tracking area i and its peripheral area. In the third embodiment, the tracking position correcting unit  105  sets the peripheral area of the tracking area i as a rectangular search range. It is assumed here that the top left coordinate of the peripheral area is denoted by (lx, ty) and the bottom right coordinate of the peripheral area is denoted by (rx, by). It is also assumed that the top left coordinate of the frame image is the origin (0, 0), the direction from the left to the right on the screen is the positive direction in the x direction, and the direction from the top to the bottom on the screen is the positive direction in the y direction. It is further assumed that the center coordinate of the tracking area i is denoted by (cx, cy), the width is denoted by w, the height is denoted by h, the width of the frame image is denoted by W, and the height of the frame image is denoted by H. In this case, lx=max (0, cx−w), rx=(min W, cx+w), ty=(max 0, cy−h), and by=min (H, cy+h). 
     Next, the tracking position correcting unit  105  defines the rectangular area having the same size as that of the tracking area i as a reference area and sets five reference areas on the frame image at random so that the center coordinate of each reference area is within the peripheral area and outside the tracking area. 
     Then, the tracking position correcting unit  105  calculates the degree of similarity between the tracking area i and each of the five reference areas using a predetermined similarity calculation formula. Any similarity calculation formula may be used as long as the similarity between the tracking area i and the reference area is capable of being digitized with the formula. In the third embodiment, color histogram intersection between the two areas is used. Specifically, the tracking position correcting unit  105  performs color subtraction of the color value of each area to a Nc color and, then, creates a histogram in each area. Then, the tracking position correcting unit  105  calculates the degree of similarity according to Equation (3) and Equation (4):
 
 D=Σ   c=0   Nc  min( a   c   ,b   c )  (3)
 
 S=D/N   p   (4)
 
     In Equation (3) and Equation (4), c denotes the color index value, a c  denotes the number of pixels each having the color index value c in one area, b c  denotes the number of pixels each having the color index value c in the other area, N p  denotes the total number of pixels in each area, and S denotes the degree of similarity between the two areas. 
     Referring back to  FIG. 12 , in Step S 1202 , the tracking position correcting unit  105  determines whether an area exists, in which a degree-of-similarity S calculated for each of the five reference areas is higher than a predetermined threshold value ThSm. If the tracking position correcting unit  105  determines that at least one area exists, in which the degree-of-similarity S calculated for each of the five reference areas is higher than the predetermined threshold value ThSm (YES in Step S 1202 ), the process goes to Step S 301 . If the tracking position correcting unit  105  determines that no area exists, in which the degree-of-similarity S calculated for each of the five reference areas is higher than the predetermined threshold value ThSm (NO in Step S 1202 ), the correction of the shift of the tracking position in Step S 300  is terminated. 
     As described above, the image processing apparatus  100  of the third embodiment performs the correction of the shift of the tracking position when the area similar to the tracking area exists around the tracking area. Consequently, according to the third embodiment, it is possible to track the human bodies without increasing the calculation load while keeping the tracking accuracy when the shift of the tracking position is difficult to occur, for example, when the similarity between the tracking area and the background area is low. 
     Fourth Embodiment 
     A fourth embodiment will now be described. 
     The cases that are not involved in an operation by the user are described above in the first to third embodiments. A case is described in the fourth embodiment in which a user&#39;s operation is reflected in the correction of the shift of the tracking position. 
       FIG. 13  is a block diagram illustrating an example of the configuration of an image processing apparatus  1300  of the fourth embodiment.  FIG. 14  is a flowchart illustrating an example of an image processing process in the image processing apparatus  1300  of the fourth embodiment.  FIG. 14  illustrates a processing flow from the detection and tracking of an object (human body) to the correction of the shift of the tracking position. The following description focuses on the difference from the first embodiment for simplicity. The same reference numerals are used in  FIG. 13  and  FIG. 14  to identify the same components and steps as those described in the above embodiments. A description of such components and steps is omitted herein. 
     The image processing apparatus  1300  of the fourth embodiment differs from the image processing apparatus  100  illustrated in  FIG. 1  in that a setting unit  1301  is added and in that the operation of a tracking unit  1302  is different from that of the tracking unit  102  in  FIG. 1 . 
     In the fourth embodiment, in Step S 1401  in  FIG. 14 , the setting unit  1301  sets a tracking position shift correcting function to “enabled” or “disabled” depending on a user&#39;s operation. Information about the setting of the tracking position shift correcting function to “enabled” or “disabled” may be stored in a memory in the tracking unit  1302  or may be stored in the storage device  107 . In the fourth embodiment, Step S 1401  is performed by the setting unit  1301  only before the first image acquisition and when the user performs a specific operation with a user interface (not illustrated). The operation with the user interface is, for example, depression of a setting button on the screen or a certain key operation. The operation with the user interface is not limited to this and may be arbitrarily set if whether Step S 1401  is performed is capable of being switched. For example, the setting unit  1301  may be configured so that Step S 1401  is performed based on the result of audio acquisition from the user with a microphone and audio recognition. Alternatively, when the setting unit  1301  acquires a gesture of the user from a video camera as a moving picture or a still image and recognizes a certain gesture through recognition of the image, the setting unit  1301  may be configured so that Step S 1401  is performed. 
     In Step S 201 , the image acquiring unit  101  performs the image acquisition. Steps S 201  to S 206  are the same as those in the first embodiment. In the fourth embodiment, Step S 203  to Step S 206  may be performed by the tracking unit  1302 . 
     After Step S 206 , in Step S 1402 , the tracking unit  1302  determines whether the tracking position shift correcting function is set to “enabled” by the user. If the tracking unit  1302  determines that the tracking position shift correcting function is set to “enabled” by the user (YES in Step S 1402 ), the process goes to Step S 300 . If the tracking unit  1302  determines that the tracking position shift correcting function is not set to “enabled” by the user (NO in Step S 1402 ), Step S 300  is not performed and the process goes to Step S 207 . The processing in Step S 300  in any of the first to third embodiments described above is performed as the operation in Step S 300 . 
     As described above, the image processing apparatus  1300  of the fourth embodiment is configured so that the tracking position shift correcting function is capable of being set to “enabled” or “disabled” based on the user&#39;s operation. Consequently, according to the fourth embodiment, the user is capable of flexibly adjusting the balance between the accuracy and the computer load depending on the use case or the like. 
     Fifth Embodiment 
     A fifth embodiment will now be described. 
     The method of setting the tracking position shift correcting function with the user interface by the user is described in the fourth embodiment as an example in which the user&#39;s operation is reflected in the correction of the shift of the tracking position. A case is described in the fifth embodiment in which the user sets an object (human body) the shift of the tracking position of which is to be corrected with the user interface. The configuration of the image processing apparatus  1300  of the fifth embodiment is the same as that illustrated in  FIG. 13 . 
       FIG. 15  is a flowchart illustrating an example of an image processing process in the image processing apparatus  1300  of the fifth embodiment.  FIG. 15  illustrates a processing flow from the detection and tracking of an object (human body) to the correction of the shift of the tracking position. The following description focuses on the difference from the first embodiment for simplicity. The same reference numerals are used in  FIG. 15  to identify the same steps as those described in the above embodiments. A description of such steps is omitted herein. 
     Referring to  FIG. 15 , in Step S 1501 , the setting unit  1301  sets an object which is specified by the user with the user interface and the shift of the tracking position of which is to be corrected as a tracking position shift correction target object. The human body is tracked as the object also in the fifth embodiment, as in the above embodiments. Specification information about the object for which the correction of the shift of the tracking position is to be performed may be stored in the memory in the tracking unit  1302  or may be stored in the storage device  107 . In the fifth embodiment, Step S 1501  is performed only before the first image acquisition and when the user performs a specific operation with the user interface. Examples of the operations with the user interface are the same as those in the fourth embodiment described above. 
     In Step S 201 , the image acquiring unit  101  performs the image acquisition. Steps S 201  to S 206  are the same as those in the first embodiment. In the fifth embodiment, Step S 203  to Step S 206  may be performed by the tracking unit  1302 . 
     After Step S 206 , in Step S 1502 , the tracking unit  1302  determines whether the tracking area i (that is, the object of the human body corresponding to the tracking area i) is set as the target of the correction of the shift of the tracking position. If the tracking unit  1302  determines that the tracking area i is set as the target of the correction of the shift of the tracking position (YES in Step S 1502 ), the process goes to Step S 300 . If the tracking unit  1302  determines that the tracking area i is not set as the target of the correction of the shift of the tracking position (NO in Step S 1502 ), Step S 300  is not performed and the process goes to Step S 207 . The processing in Step S 300  in any of the first to third embodiments described above is performed as the operation in Step S 300 . 
     As described above, in the image processing apparatus  1300  of the fifth embodiment, the tracking area to which the tracking position shift correcting function is applied is set based on the user&#39;s operation. Consequently, according to the fifth embodiment, the user is capable of adjusting the accuracy for each human body to be tracked depending on the use case or the like and is capable of flexibly adjusting the balance between the accuracy and the computer load. 
     Sixth Embodiment 
     A sixth embodiment will now be described. 
     The example is described in the fifth embodiment in which the user sets an object the shift of the tracking position of which is to be corrected with the user interface. A case is described in the sixth embodiment in which the user sets an area to which the correction of the shift of the tracking position is applied with the user interface. The configuration of the image processing apparatus of the sixth embodiment is the same as that illustrated in  FIG. 13 . 
       FIG. 16  is a flowchart illustrating an example of an image processing process in the image processing apparatus of the sixth embodiment.  FIG. 16  illustrates a processing flow from the detection and tracking of an object (human body) to the correction of the shift of the tracking position. The following description focuses on the difference from the first embodiment for simplicity. The same reference numerals are used in  FIG. 16  to identify the same steps as those described in the above embodiments. A description of such steps is omitted herein. 
     Referring to  FIG. 16 , in Step S 1601 , the setting unit  1301  sets an image area which is specified by the user with the user interface and in which the shift of the tracking position is to be corrected. Specification information about the image area in which the correction of the shift of the tracking position is to be performed may be stored in the memory in the tracking unit  1302  or may be stored in the storage device  107 . In the sixth embodiment, Step S 1601  is performed only before the first image acquisition and when the user performs a specific operation with the user interface. Examples of the operations with the user interface are the same as those in the fourth embodiment described above. 
     In Step S 201 , the image acquiring unit  101  performs the image acquisition. Steps S 201  to S 206  are the same as those in the first embodiment. In the sixth embodiment, Step S 203  to Step S 206  may be performed by the tracking unit  1302 . 
     After Step S 206 , in Step S 1602 , the tracking unit  1302  determines whether the position of the tracking area i is within the image area set in Step S 1601 . If the tracking unit  1302  determines that the position of the tracking area i is within the image area set in Step S 1601  (YES in Step S 1602 ), the process goes to Step S 300 . If the tracking unit  1302  determines that the position of the tracking area i is not within the image area set in Step S 1601  (NO in Step S 1602 ), Step S 300  is not performed and the process goes to Step S 207 . The processing in Step S 300  in any of the first to third embodiments described above is performed as the operation in Step S 300 . 
     As described above, in the image processing apparatus  1300  of the sixth embodiment, the area to which the correction of the shift of the tracking position is applied is set based on the user&#39;s operation. Consequently, according to the sixth embodiment, the user is capable of adjusting the accuracy for each image area and flexibly adjusting the balance between the accuracy and the computer load depending on the use case or the like. 
     Seventh Embodiment 
     A seventh embodiment will now be described. 
     The configuration of the image processing apparatus of the seventh embodiment is the same as that in  FIG. 1  or  FIG. 13  and a description of the components is omitted herein. The configuration in  FIG. 1  is exemplified in the seventh embodiment for description. 
     In the seventh embodiment, the tracking unit  102  selects multiple tracking position candidates in estimation of the tracking position of the concerned human body. A method of more effectively suppressing the shift of the tracking position, compared with the first embodiment, by selecting the multiple tracking position candidates in the estimation of the tracking position of the concerned human body is described in the seventh embodiment. 
     Non-maximum suppression (NMS) disclosed in Pedro F Felzenszwalb, Ross B Girshick, and David McAllester, (2010) “Object detection with discriminatively trained part-based models,” TPAMI, Vol. 32, No. 9 is exemplified as a method of determining the final detection result from the multiple tracking position candidates. 
     In the NMS, if the value of intersection-over-union (IoU) between a certain concerned region and a region having a higher score, among multiple scored regions in an image, is higher than or equal to a predetermined threshold value, rejection of the concerned region is repeatedly performed. The IoU is a value representing the ratio of overlapping of images. The IoU is represented by Equation (5):
 
IoU=(Region  A ∧Area of region  B )/(Region  A ∨Area of region  B )   (5)
 
     A particle filter method disclosed in Genshiro Kitagawa (1996) “On Monte Carlo Filter and Smoother” Statistical Mathematics, Vol 44, No. 1, pp. 31-48 may be used as the method of determining the final detection result from the multiple tracking position candidates. In the particle filter method, multiple subsequent states that may occur from the current state are represented by many particles. In the particle filter method, weighted average of all particles is calculated based on the likelihood of each particle and the calculated weighted average is used as the subsequent state (the tracking position). 
     Alternatively, for example, a method of calculating the average of the multiple tracking position candidates to determine the final detection result from the multiple tracking position candidates may be used as the method of determining the final detection result from the multiple tracking position candidates. In this method of calculating the average, the average value of each of the center coordinates, the widths, and the heights of the tracking position candidates is calculated and the reference area having the calculated values is the detection result. 
     However, in the above methods, if the area similar to a human body to be detected exists around the human body, the similar area may be erroneously detected as the tracking position candidate. The tracking position candidate that is erroneously detected causes the shift of the detection position in the final detection result. 
       FIG. 17A  to  FIG. 17C  are diagram illustrating an example of the general process of detecting the tracking target objects (the human bodies in this example) and a final detection result.  FIG. 17A  illustrates an example of a frame image  1700  before the detection and tracking process.  FIG. 17B  illustrates an image during the detection and tracking process.  FIG. 17C  illustrates an image after the detection and tracking process. The frame image  1700  in  FIG. 17A  is part of a frame image including detection target objects and human bodies  1701  and  1702  are concerned human bodies, which is the detection target objects. Referring to  FIG. 17B , dotted-line bounding boxes indicate multiple tracking position candidates  1710  before the detection result is determined and a solid-line bounding box indicates a tracking position candidate  1711  that is erroneously detected. In the seventh embodiment, the detection result having a score higher than or equal to a predetermined value is the tracking position candidate. A bold-solid-line bounding box in  FIG. 7C  indicates an example of a final detection result  1720  in the general process of detecting the tracking target objects. 
     Although the final detection result should be a bounding box surrounding the human body  1701  that is being detected and tracked in the example in  FIG. 17A  to  FIG. 17C , the final detection result  1720  is shifted toward the top right due to the tracking position candidate  1711  that is erroneously detected. 
       FIG. 18A  and  FIG. 18B  are diagrams illustrating the process of detecting the tracking target objects (the human bodies) for suppressing the shift of the tracking position and a final detection result in the seventh embodiment.  FIG. 18A  illustrates part of a frame image  1800  to be detected and tracked. Dotted-line bounding boxes in  FIG. 18A  indicate multiple tracking position candidates  1810  before the detection result is determined.  FIG. 18B  illustrates an image after the detection and tracking process. 
     A method of determining the tracking position candidates in the seventh embodiment will now be described. First process to Fourth process are sequentially performed in the determination of the tracking position candidates. 
     First process: Only detection results each having a score or likelihood higher than or equal to a predetermined value are left as the tracking position candidates. 
     Second process: Moving estimated regions on the concerned frame are calculated based on the tracking results to the preceding frame for the tracking object (the tracking human body). 
     Third process: The IoU values between the regions of the tracking position candidates in First process and the respective moving estimated regions calculated in Second process are calculated. 
     Fourth process: Any tracking position candidate having an IoU value exceeding a predetermined threshold value in Third process is deleted regardless of its score or likelihood. 
     The tracking position candidate that is left after Fourth process is a final detection result  1820  indicated by a bold-solid-line bounding box in  FIG. 18B . In the seventh embodiment, the final detection result  1820  is created through weighted averaging based on the scores of the tracking position candidates. The final detection result  1820  is a bounding box surrounding the human body that is being detected and tracked in the seventh embodiment. 
     As described above, the image processing apparatus  100  of the seventh embodiment selects the multiple tracking position candidates before the detection result is determined in the detection of the tracking target object. Consequently, according to the seventh embodiment, it is possible to suppress the shift of the tracking position more effectively. 
     The components in the image processing apparatus or the processing in the flowchart in each of the embodiments described above may be realized by hardware components or may be realized by software components. In the case of the software components, for example, a central processing unit (CPU) executes a program according to the embodiment. Alternatively, part of components or the processing in the flowchart may be realized by the hardware components and part thereof may be realized by the software components. The program for the software components may be prepared in advance, may be acquired from a recording medium, such as an external memory (not illustrated), or may be acquired via a network or the like (not illustrated). 
     Among the components in the image processing apparatus in each of the embodiments described above, the processing performed in, for example, the tracking unit, the locus analyzing unit, the locus smoothing unit, and the tracking position correcting unit may be processing to which artificial intelligence (AI) is applied. For example, a learned model that is subjected to machine learning may be used, instead of the components. In this case, the learned model is created, in which multiple combinations of input data and output data into and from the respective components are prepared as learning data, knowledge is acquired through the machine learning, and the output data corresponding to the input data is output as the result based on the acquired knowledge. The learned model is capable of being composed of, for example, a neural network. The learned model operates cooperatively with, for example, a CPU or a graphics processing unit (GPU) as a program to perform the same processing as in the components described above to perform the processing in the components described above. The learned model may be, for example, updated each time a certain amount of data is processed, if needed. 
     Other Embodiments 
     Embodiments of the present disclosure 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 embodiments 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 embodiments, 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 embodiments and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiments. 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 disclosure 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. 2019-140086, filed on Jul. 30, 2019, which is hereby incorporated by reference herein in its entirety.