Patent Publication Number: US-7715476-B2

Title: System, method and article of manufacture for tracking a head of a camera-generated image of a person

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 10/353,858, entitled SYSTEM, METHOD AND ARTICLE OF MANUFACTURE FOR TRACKING A HEAD OF A CAMERA-GENERATED IMAGE OF A PERSON filed Jan. 28, 2003 now U.S. Pat. Ser. No. 6,909,455 which is incorporated herein by reference for all purposes, which is a continuation of U.S. patent application Ser. No. 09/364,859, entitled SYSTEM, METHOD AND ARTICLE OF MANUFACTURE FOR TRACKING A HEAD OF A CAMERA-GENERATED IMAGE OF A PERSON filed Jul. 30, 1999 now U.S. Pat. No. 6,545,706 which is incorporated herein by reference for all purposes. 
     This application is related to a U.S. patent application filed Jul. 30, 1999 with the title “SYSTEM, METHOD AND ARTICLE OF MANUFACTURE FOR DETECTING COLLISIONS BETWEEN VIDEO IMAGES GENERATED BY A CAMERA AND AN OBJECT DEPICTED ON A DISPLAY” and Katerina H. Nguyen listed as inventor; a U.S. patent application filed Oct. 15, 1997 under Ser. No. 08/951,083 with the title “A SYSTEM AND METHOD FOR PROVIDING A JOINT FOR AN ANIMATABLE CHARACTER FOR DISPLAY VIA A COMPUTER SYSTEM”; and a U.S. patent application filed Jul. 30, 1999 with the title “WEB BASED VIDEO ENHANCEMENT APPARATUS, METHOD, AND ARTICLE OF MANUFACTURE” and Subutai Ahmad and Jonathan Cohen listed as inventors and which are all incorporated herein by reference in their entirety. 
    
    
     BACKGROUND OF THE INVENTION 
     1. The Field of the Invention 
     The present invention relates to displaying video images generated by a camera on a display, and more particularly to tracking a head portion of a person image in camera-generated video images. 
     2. The Relevant Art 
     It is common for personal computers to be equipped with a camera for receiving video images as input. Conventionally, such camera is directed toward a user of the personal computer so as to allow the user to view himself or herself on a display of the personal computer during use. To this end, the user is permitted to view real-time images that can be used for various purposes. 
     One purpose for use of a personal computer-mounted camera is to display an interaction between camera-generated video images and objects generated by the personal computer and depicted on the associated display. In order to afford this interaction, a current position of the user image must be identified. This includes identifying a current position of the body parts of the user image, including the head. Identification of an exact current location of the user image and his or her body parts is critical for affording accurate and realistic interaction with objects in the virtual computer-generated environment. In particular, it is important to track a head portion of the user image since this specific body part is often the focus of the most attention. 
     Many difficulties arise, however, during the process of identifying the current position of the head portion of the user image. It is often very difficult to discern the head portion when relying on a single technique. For example, when identifying the location of a head portion using shape, color, motion etc., portions of the background image and the remaining body parts of the user image may be confused with the head. For example, a flesh coloring of a hand may be mistaken for features of the head. 
     SUMMARY OF THE INVENTION 
     A system, method and article of manufacture are provided for tracking a head portion of a person image in video images. Upon receiving video images, a first head tracking operation is executed for generating a first confidence value. Such first confidence value is representative of a confidence that a head portion of a person image in the video images is correctly located. Also executed is a second head tracking operation for generating a second confidence value representative of a confidence that the head portion of the person image in the video images is correctly located. The first confidence value and the second confidence value are then outputted. Subsequently, the depiction of the head portion of the person image in the video images is based on the first confidence value and the second confidence value. 
     In one embodiment of the present invention, the first head tracking operation begins with subtracting a background image from the video images in order to extract the person image. Further, a mass-distribution histogram may be generated that represents the extracted person image. A point of separation is then identified between a torso portion of the person image and the head portion of the person image. 
     Next, the first head tracking operation continues by identifying a top of the head portion of the person image. This may be accomplished by performing a search upwardly from the point of separation between the torso portion and the head portion of the person image. Subsequently, sides of the head portion of the person image are also identified. As an option, the first head tracking operation may track the head portion of the person image in the video images using previous video images including the head portion of the person image. 
     In one embodiment, the second head tracking operation may begin by identifying an initial location of the head portion of the person image in the video images. Thereafter, a current location of the head portion of the person image may be tracked starting at the initial location. As an option, the initial location of the head portion of the person image may be identified upon each instance that the second confidence value falls below a predetermined amount. By this feature, the tracking is “restarted” when the confidence is low that the head is being tracked correctly. This ensures improved accuracy during tracking. 
     As an option, the initial location of the head portion of the person image may be identified based on the detection of a skin color in the video images. This may be accomplished by extracting a flesh map; filtering the flesh map; identifying distinct regions of flesh color on the flesh map; ranking the regions of flesh color on the flesh map; and selecting at least one of the regions of flesh color as the initial location of the head portion of the person image based on the ranking. During such procedure, holes in the regions of flesh color on the flesh map may be filled. Further, the regions of flesh color on the flesh map may be combined upon meeting a predetermined criteria. 
     In a similar manner, the current location of the head portion of the person image may be tracked based on the detection of a skin color in the video images. Such technique includes extracting a sub-window of the head portion of the person image in the video images; forming a color model based on the sub-window; searching the video images for a color similar to the color model; and estimating the current location of the head portion of the person image based on the search. 
     In one embodiment, the module that identifies the initial location of the head portion of the person image and the module that identifies the current location of the head portion of the person image may work together. In particular, while tracking the current location of the head portion of the person image, a flesh map may be obtained. Thereafter, the flesh map may be used during subsequent identification of an initial location of the head portion of the person image when the associated confidence level drops below the predetermined amount. 
     Similar to using the skin color, the initial location of the head portion of the person image may also be identified based on the detection of motion in the video images. Such identification is achieved by creating a motion distribution map from the video images; generating a histogram based on the motion distribution map; identifying areas of motion using the histogram; and selecting at least one of the areas of motion as being the initial location of the head portion of the person image. 
     Similarly, the current location of the head portion of the person image may be tracked based on the detection of motion in the video images. This may be accomplished by determining a search window based on a previous location of the head portion of the person image; creating a motion distribution map within the search window; generating a histogram based on the distribution motion map; identifying areas of motion using the histogram; and selecting at least one of the areas of motion as being the initial location of the head portion of the person image. 
     These and other aspects and advantages of the present invention will become more apparent when the Description below is read in conjunction with the accompanying Drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention will be readily understood by the following detailed description in conjunction with the accompanying drawings, with like reference numerals designating like elements. 
         FIG. 1  is a schematic diagram illustrating an exemplary hardware implementation in accordance with one embodiment of the present invention; 
         FIG. 2  illustrates a flowchart of a process for tracking a head portion of a person image in camera-generated video images in accordance with one embodiment of the present invention; 
         FIG. 3  shows a flow chart for a first head tracking operation that tracks a head portion of a person image in camera-generated video images using background subtraction in accordance with one embodiment of the present invention; 
         FIG. 4  illustrates a flow chart for a process of the present invention which carries out the scene parsing operation  304  of  FIG. 3 ; 
         FIG. 5  illustrates a flow chart for a process of the present invention which carries out operation  306  of  FIG. 3 ; 
         FIG. 5A  is an illustration of a y-axis histogram generated in operation  500  shown in  FIG. 5 . 
         FIG. 6  shows a flow chart for a second head tracking operation that tracks a head portion of a person image in camera-generated video images using capture and tracker routines in accordance with one embodiment of the present invention; 
         FIG. 7  shows a flow chart for a process of the present invention associated with the skin detection operation  604  of  FIG. 6 ; 
         FIG. 7A  illustrates a person image of the video images, as inputted into the extract flesh map operation  702  of  FIG. 7 ; 
         FIG. 7B  illustrates a raw flesh map, as outputted from the extract flesh map operation  702  of  FIG. 7 ; 
         FIG. 7C  illustrates a flesh map, as outputted from the fill holes operation  710  of  FIG. 7 ; 
         FIG. 7D  illustrates a flesh map, as outputted from the combine regions operation  714  of  FIG. 7 ; 
         FIG. 8  illustrates a flow chart for a process of the present invention associated with the generate hypothesis operation  716  of  FIG. 7 ; 
         FIG. 9  shows a flow chart for a process of the present invention associated with the motion detection operation  606  of  FIG. 6 ; 
         FIG. 10  shows a flow chart for a process of the present invention associated with the color follower operation  604  of  FIG. 6 ; 
         FIG. 10A  illustrates a sub-window of the present invention associated with operation  1000  of  FIG. 10 ; 
         FIG. 10B  shows an RGB histogram of the present invention outputted for each pixel within the image sub-window of  FIG. 10B  as a result of operation  1006  of  FIG. 10 ; 
         FIG. 10C  is an illustration of a previous verified head rectangle and a search grid generated therefrom in operation  1009  of  FIG. 10 ; 
         FIG. 11  shows a flow chart for a process of the present invention associated with the perform search operation  1016  of  FIG. 10 ; 
         FIG. 11A  shows the search grid and the areas involved with the process of  FIG. 11 ; 
         FIG. 12  illustrates a flow chart for a process of the present invention associated with a feedback process between the color follower operation  612  and the skin detection operation  604  of  FIG. 6 ; and 
         FIG. 13  shows a flow chart for a process of the present invention associated with the motion follower operation  610  of  FIG. 6 . 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The present invention affords a technique for tracking a head portion of a person image in camera-generated video images. This is accomplished using at least two head tracking operations that each track the head portion of the person image in camera-generated video images. In addition, each head tracking operation further generates a confidence value that is indicative of a certainty that the head portion of the person image is being tracked correctly. This information may be used by an associated application for depicting an interaction between the head and a virtual computer-generated environment. 
       FIG. 1  shows an exemplary hardware configuration in accordance with one embodiment of the present invention where a central processing unit  110 , such as a microprocessor, and a number of other units interconnected via a system bus  112 . The hardware configuration shown in  FIG. 1  includes Random Access Memory (RAM)  114 , Read Only Memory (ROM)  116 , an I/O adapter  118  for connecting peripheral devices such as disk storage units  120  to the bus  112 , a user interface adapter  122  for connecting a keyboard  124 , a mouse  126 , a speaker  128 , a microphone  132 , a camera  133  and/or other user interface devices to the bus  112 , communication adapter  134  for connecting the hardware configuration to a communication network  135  (e.g., a data processing network) and a display adapter  136  for connecting the bus  112  to a display device  138 . 
     The hardware configuration typically has resident thereon an operating system such as the Microsoft Windows NT or Windows/98/2000 Operating System (OS), the IBM OS/2 operating system, the MAC OS, or UNIX operating system. Those skilled in the art will appreciate that the present invention may also be implemented on platforms and operating systems other than those mentioned. For example, a game system such as a SONY PLAYSTATION or the like may be employed. Yet another example includes an application specific integrated circuit (ASIC) or any other type of hardware logic that is capable of executing the processes of the present invention. Further, in one embodiment, the various processes employed by the present invention may be implemented using C++ programming language or the like. 
       FIG. 2  illustrates a flowchart of a process for tracking a head portion of a person image in camera-generated video images in accordance with one embodiment of the present invention. As shown, upon receiving video images generated by a camera, a first head tracking operation  200  is executed for generating a first confidence value. It should be noted that the video images may be generated by the camera at any time and not necessarily immediately before being received by the head tracking operation. Further, the video images may be partly computer enhanced or completely computer generated per the desires of the user. 
     The first confidence value generated by the first head tracking operation is representative of a confidence that a head portion of a person image in the camera-generated video images is located. Also executed is a second head tracking operation  202  for generating a second confidence value representative of a confidence that the head portion of the person image in the camera-generated video images is located. 
     The first confidence value and the second confidence value may then be made available for use by various applications in operation  204 . Such applications may decide whether the head portion of the person image has moved based on the confidence values. Logic such as an AND operation, an OR operation, or any other more sophisticated logic may be employed to decide whether the results of the first head tracking operation and/or the second head tracking operation are indicative of true head movement. 
     For example, if at least one of the head tracking operations indicates a high confidence of head movement, it may be decided to assume that the head has moved. On the other hand, if both head tracking operations indicate a medium confidence of movement, it may be assumed with similar certainty that the head has moved. If it is decided to assume that the head has moved, an interaction may be shown between the video images generated by the camera and the virtual computer-generated environment. 
       FIG. 3  shows a flow chart for a process associated with the first head tracking operation  200 . In use, the first head tracking operation  200  tracks a head portion of a person image in camera-generated video images using background subtraction. As shown, in operation  300 , the first head tracking operation begins by obtaining a foreground by subtracting a background image from the video images generated by the camera. This may be accomplished by first storing the background image, or model  302 , without the presence of the person image. Then, a difference may be found between a current image and the background image. More information on the background model and background subtraction may be found in a patent application entitled “METHOD AND APPARATUS FOR MODEL-BASED COMPOSITING” filed Oct. 15, 1997 under application Ser. No. 08/951,089 which is incorporated herein by reference in its entirety. 
     Next, in operation  304 , a “scene parsing” process is carried which identifies a location and a number of person images in the video images. This is accomplished by utilizing a person image, or foreground mask(s), that is generated by the background subtraction carried out in operation  300  of  FIG. 3 . Additional information will be set forth regarding the “scene parsing” process with reference to  FIG. 4 . Finally, the head portion is found for each person image in operation  306  that will be set forth in greater detail with reference to  FIG. 5 . 
       FIG. 4  illustrates a flow chart for a process of the present invention which carries out the scene parsing operation  304  of  FIG. 3 . As shown, in operation  400 , the subtracted image, or foreground mask(s), is first received as a result of the background subtraction operation  300  of  FIG. 3 . Next, in operation  402 , the foreground mask(s) is filtered using a conventional median filter to create a mass distribution map. 
       FIG. 4A  is an illustration of a mass distribution  404  used in the scene parsing process of  FIG. 4 . As shown, the mass distribution  404  indicates a number of pixels, or a pixel density, along the horizontal axis of the display that do not represent the background image. In the mass distribution  404  of  FIG. 4A , a curve  406  of the mass distribution  404  has a plurality of peaks  408  which represent high concentrations of pixels along the horizontal axis that do not correspond to the background image and, possibly, a person image or other objects. 
     With continuing reference to  FIG. 4 , in operation  410 , portions of the mass distribution  404  are eliminated if they do not surpass a predetermined threshold. This ensures that small peaks  408  of the curve  406  of the mass distribution  404  having a low probability of being a person image are eliminated. Next, it is then determined whether a previous mass distribution  404 , or history, is available in memory. Note decision  412 . 
     If a history is available, the location and number of person images in the video images are identified based on a frame difference between the peaks  408  of a previous mass distribution and the peaks  408  of the current mass distribution  404 , as indicated in operation  414 . 
     On the other hand, if the history is not available in decision  412 , the peaks  408  of the current mass distribution  404  are considered person images in operation  416 . In any case, the location and number of person images that are assumed based on the peaks  408  of the mass distribution  404  are stored in operation  418 . Further information may be found regarding scene parsing and locating person images in the video images in a U.S. patent application filed Jul. 30, 1999 with the title “SYSTEM, METHOD AND ARTICLE OF MANUFACTURE FOR DETECTING COLLISIONS BETWEEN VIDEO IMAGES GENERATED BY A CAMERA AND AN OBJECT DEPICTED ON A DISPLAY” which is incorporated herein by reference in its entirety. Once the person image(s) have been located in the video images generated by the camera, it is then required that the head portion of each person image be located. 
       FIG. 5  illustrates a flow chart for a process of the present invention which carries out operation  306  of  FIG. 3 . Such process starts in operation  500  by generating a mass-distribution histogram that represents the extracted person image.  FIG. 5A  is an illustration of the histogram  501  generated in operation  500  shown in  FIG. 5 . For reasons that will soon become apparent, it is important that the histogram be formed along a y-axis. 
     With continuing reference to  FIG. 5 , a point of separation  502  (See  FIG. 5A ) is then identified in operation  504  between a torso portion of the person image and the head portion of the person image. Next, a top of the head portion of the person image is identified in operation  506 . This may be accomplished by performing a search upwardly from the point of separation between the torso portion and the head portion of the person image. Subsequently, sides of the head portion of the person image are also identified in operation  508 . 
     It is then determined in decision  510  whether any history exists with respect to the previous head size and location of each person image. Such history may take the form of previous head sizes and locations stored in memory. If it is determined in decision  510  that there is history, it is then determined in decision  512  whether the current head size and location is consistent with the historical head size and location, taking into account motion of the person image and a time duration between frames. If no consistency exists, it is assumed that the current head size and location is erroneous and a bounding box is generated based on the historical head size and location in operation  514 . It should be noted that it is the bounding box that defines the estimated location and size of the head of the person image. 
     If, on the other hand, it is decided in operation  512  that the current head size and location is similar to the historical head size and location, or it is decided in operation  510  that there is no history, a confidence score associated with the head bounding box is generated based on mass distribution, shape, consistency with history, consistency with body proportions, etc. Note operation  514 . It should be noted that the first confidence value associated with the first head tracking operation may be based at least in part on the foregoing confidence. After operation  514 , the history is updated to include the current mass distribution if a confidence value of the head bounding box is above a predetermined threshold in operation  516 . 
       FIG. 6  shows a flow chart for a second head tracking operation that tracks the head portion of the person image by way of a capture routine  600  and a tracker routine  602 . As shown, the second head tracking operation may begin by identifying an initial location of the head portion of the person image in the camera-generated video images based on the detection of skin in operation  604 . Further, the initial location may also be identified based on motion detection in operation  606 . It should be noted that any other types of detection methods may be used in lieu of or in combination with the skin and motion detection operations. 
     Thereafter, a Support-Vector Networks (SVM) head verifier routine is executed in operation  608  in order to verify that the head portion has been identified after reviewing the detected parameters, e.g., motion, skin color, etc. Such verifier routine is commonly known to those of ordinary skill. Further, additional details regarding such operation may be found with reference to “Support-Vector Networks”, by C. Cortes and V. Vapnik, in “Machine Learning”, Vol. 20 (1995), pp. 273-297, which is incorporated herein by reference in its entirety. 
     Once the initial location of the head portion of the person image has been identified, or captured, the head tracker operation  602  is executed to continuously track a current location of the head portion of the person image. As shown in  FIG. 6 , the current location of the head portion of the person image may be tracked starting at the initial location based on motion in operation  610  and based on color in operation  612 . 
     Similar to the head verifier operation  608  of the head capture routine  600 , a head verifier routine is also executed in the head tracker routine  602  in operation  614  in order to verify that the current location head portion has been identified after reviewing the detected parameters, e.g., motion, skin color, etc. Again, such verifier routine is commonly known to those of ordinary skill. Further details regarding such operation may be found with reference to “Indexing Via Color Histograms”, by M. J. Swain and D. H. Ballard, in Proceedings of 1990 International Conf. on Computer Vision, p. 390-393, which is incorporated herein by reference in its entirety. 
     During the course of the head tracker routine  602 , the initial location of the head portion of the person image may be identified upon each instance that the second confidence value falls below a predetermined amount. By this feature, the tracking is “restarted” and the head portion of the person image is re-captured when the confidence that the head is being tracked correctly is low. This ensures improved accuracy during tracking. This feature is facilitated by a feedback loop  616  shown in  FIG. 6 . When the initial location of the head portion of the person image need not be identified, the head capture routine  600  may be skipped via path  618 . 
       FIG. 7  shows a flow chart for a process of the present invention associated with the skin detection operation  604  of  FIG. 6 , wherein the initial location of the head portion of the person image may be identified based on the detection of a skin color in the video images. This may be accomplished by receiving a person image  700  (See  FIG. 7A ) and extracting a raw flesh map in operation  702 .  FIG. 7B  illustrates an example of a flesh map  704  which is generated using the person image  700  of  FIG. 7A . 
     With continuing reference to  FIG. 7 , the flesh map is filtered using a conventional median filter in operation  706 . Next, in operation  708 , distinct regions of flesh color on the flesh map are identified using a standard “connected components algorithm” or any other desired technique. Such regions may then be stored in a list which includes information on each of the regions, e.g. size, neighboring region, etc. 
     Outputs of operations  706  and  708  are subsequently used to fill holes in the regions with the flesh color of the surrounding region in operation  710 . Such holes are areas which are fully encompassed by the regions and that do not exhibit the flesh color.  FIG. 7C  illustrates a flesh map, as outputted from the fill holes operation  710  of  FIG. 7 . 
     Thereafter, in operation  712 , regions of the flesh color are selected, or extracted, that exceed a predetermined minimum size. Upon selection, the aforementioned list is updated to reflect only the selected regions. Next, the regions are combined, or logically associated into a group, based on a proximity of the regions to other regions and the resulting shape of the regions when combined. See operation  714 . Again, the list is updated to reflect only the combined regions.  FIG. 7D  illustrates a flesh map with combined regions  715 , as outputted from the combine regions operation  714  of  FIG. 7 . 
     With the regions combined, the associated list is used to generate a hypothesis as to which of the regions represents a head portion of a corresponding person image in operation  716 . Further details regarding operation  716  will be set forth in greater detail hereinafter with reference to  FIG. 8 . After the hypothesis is generated, the hypothesis is evaluated in operation  718 . 
       FIG. 8  illustrates a flow chart for a process of the present invention associated with the hypothesis generation operation  716  of  FIG. 7 . As shown, such process begins in operation  800  by generating a score for each region using the list, as edited in operation  714  of  FIG. 7 . In other words, the regions of flesh color on the flesh map are ranked. Such ranking is based at least partly on a degree of similarity between the regions and a predefined oval. In one embodiment, such oval may have a 3/2:1 height to width ratio. 
     Next, in operation  802 , the regions are combined in every possible permutation. The scores for the regions of each permutation are then multiplied to render resultant scores used to select which region represents the head portion of the person image. 
       FIG. 9  shows a flow chart for a process of the present invention associated with the motion detection operation  606  of  FIG. 6 . As shown, in operation  900 , a motion distribution map is first generated from the video images. In a preferred embodiment, the motion distribution map is converted into a summed-area table for acceleration purposes in operation  902 . Further information may be found on summed-area tables with reference to F. C. Crow. Summed-area tables for texture mapping. Computer Graphics, 18(3), 207-212 (1984) which is incorporated herein by reference in its entirety. 
     With continuing reference to  FIG. 9 , a histogram is then generated that is similar to that shown in  FIG. 5A  with the exception of the addition of a component along the x-axis. Note operation  904 . A number of objects resembling a head portion of a person image are then identified from the histogram in operation  906 . Exemplary criteria used for such identification includes peaks in the histogram. A “best fit” is then found amongst the identified objects in operation  908 . 
       FIG. 10  shows a flow chart for a process of the present invention associated with the color follower operation  612  of  FIG. 6 . Upon receipt of a current image and a verified head rectangle from the head verifier operation  608  of  FIG. 6 , an image sub-window is selected within the verified head rectangle in operation  1000 .  FIG. 10A  illustrates a sub-window  1002  of a size of 15×20 pixels which is arbitrarily smaller than the associated current image  1004  which has a size of 120×160 pixels. 
     A histogram is then generated based on the contents of the image sub-window  1002  in operation  1006 .  FIG. 10B  shows an RGB histogram  1008  outputted for each pixel within the image sub-window  1002  as a result of operation  1006 . In order to condense the histogram  1008 , each axis may be divided into uniform increments to form multiple intervals. In one embodiment, each axis may have 16 intervals between 0-255. The histograms of the pixels may then be used to construct a look-up table, or color model, which indicates in which interval the R, G, and B components of each pixel exists. 
     Next, in operation  1009 , a previous verified head rectangle is used to set up a search grid.  FIG. 10C  is an illustration of the previous verified head rectangle  1010  and the search grid  1012 . The search grid  1012  is generally larger than the previous verified head rectangle  1010 . Such size difference is governed by a rate at which the camera accepts images and a potential amount of movement of the person image during the intervals between the images. 
     With continuing reference to  FIG. 10 , the color model, the current image, and the search grid are used to perform a search in operation  1016 . Such search identifies a window within the search grid  1012  that best matches the color model. A raw similarity map is generated based on the contents of the best matching window. Once the best matching window is selected, it is smoothed in operation  1018 . Finally, a portion of the raw similarity map that has the best score is chosen as the best head estimate in operation  1020  after which a confidence is generated. Such confidence is indicative of a certainty that the head is being tracked correctly based on a shape of a peak of the smooth similarity map that corresponds to the best head estimate. It should be noted that the second confidence value associated with the second head tracking operation may be based at least in part on the foregoing confidence. Additional details will be set forth regarding the foregoing “tracker” operations hereinafter with reference to  FIG. 11 . 
     It should be noted that the “capture” operations  1000 - 1006  are carried out on average once every 30 frames. In contrast, the following “tracker” operations  1008 - 1020  are repeated every frame using the latest color model from the “capture” operations  1000 - 1006 . While the “capture” operations  1000 - 1006  are carried out on average once every 30 frames, such operations are repeated based on an ability of the “tracker” operations  1008 - 1020  to function properly. 
       FIG. 11  shows a flow chart for a process of the present invention associated with the perform search operation  1016  of  FIG. 10 . As shown, a grid point is first selected within the search grid in operation  1110 . Thereafter, in operation  1112 , a 3-D histogram is then generated for each point of the search grid. Such 3-D histogram corresponds to a rectangle within the search grid with the selected grid point at a corner thereof. Each of the foregoing 3-D histograms is then compared to the color model in operation  1114  after which a score is assigned to the histogram in operation  1116 . This procedure is continued until it is decided in operation  1118  that all of the grid points have been selected. As mentioned earlier with reference to  FIG. 10 , the histogram with the greatest score is considered the best head estimate. 
       FIG. 11A  shows the search grid  1012 , and the areas  1104  in which the 3-D histograms are generated. As shown, areas  1014  of adjacent grid points  1016  have an overlapping portion  1018 . In a preferred embodiment, the histogram is generated only once for each overlapping portion  1018  to incur a significant processing time savings. 
       FIG. 12  illustrates a flow chart for a process of the present invention associated with a feedback process between the color follower operation  612  and the skin detection operation  604  of  FIG. 6 . In particular, while tracking the current location of the head portion of the person image in operation  702  of  FIG. 7 , a flesh map may be obtained in operation  1200 . Ideally, the flesh map generated in the present process is only a two-dimensional map R and G, where |R|=R/(R+G+B) and |G|=G/(R+G+B). Thereafter, in operation  1202 , a “best-fit” oval is found in the two-dimensional flesh map. Noise in the form of dots outside boundaries of the oval are therefore removed. The oval is then filled in operation  1206 . At this point, the flesh map is adapted to be fed back from the color follower operation  612  to the skin capture operation  604  via a feedback  1208  shown in  FIG. 7 . 
     It should be noted that the process of  FIG. 12  is executed during a first cycle of the second head tracking operation. Further, the process may be used repeatedly during subsequent identification of an initial location of the head portion when the associated confidence level drops below a predetermined amount. 
       FIG. 13  shows a flow chart for a process of the present invention associated with the motion follower operation  610  of  FIG. 6 . As shown, the current location of the head portion of the person image may be tracked based on the detection of motion in the video images. This may be accomplished by first determining a search window based on a previous location of the head portion of the person image in operation  1300 . This is accomplished in a manner similar to that in which the search window shown in  FIG. 10C  is generated in operation  1000  of  FIG. 10 . It should be noted that the previous location may be stored in a history  1301 . 
     Next, a motion distribution map is created within the search window in operation  1302 . A y-axis histogram is then generated based on the distribution motion map. Ideally, the histogram is smoothed before identifying areas of motion. In operation  1308 , at least one of the areas of motion is selected as being the initial location of the head portion of the person image. Selection is governed by multiple factors including a position of the motion, proportions of the motion, and the position of the motion relative to other motion. The more of such factors that indicate a motion is a head, the higher the confidence and certainty that the head is being judged correctly. If such confidence is sufficient, the history  1301  may be updated with the current bounding box that is outputted. It should be noted that the second confidence value associated with the second head tracking operation may be based at least in part on the foregoing confidence. 
     While this invention has been described in terms of several preferred embodiments, it is contemplated that alternatives, modifications, permutations, and equivalents thereof will become apparent to those skilled in the art upon a reading of the specification and study of the drawings. It is therefore intended that the true spirit and scope of the present include all such alternatives, modifications, permutations, and equivalents.