Abstract:
Generally, techniques for analyzing foreground-segmented images are disclosed. The techniques allow clusters to be determined from the foreground-segmented images. New clusters may be added, old clusters removed, and current clusters tracked. A probabilistic framework is used for the analysis of the present invention. A method is disclosed that estimates cluster parameters for one or more clusters determined from an image comprising segmented areas, and evaluates the cluster or clusters in order to determine whether to modify the cluster or clusters. These steps are generally performed until one or more convergence criteria are met. Additionally, clusters can be added, removed, or split during this process. In another aspect of the invention, clusters are tracked during a series of images, and predictions of cluster movements are made.

Description:
FIELD OF THE INVENTION  
         [0001]    The present invention relates to computer vision and analysis, and more particularly, to a computer vision method and system for blob-based analysis using a probabilistic framework.  
         BACKGROUND OF THE INVENTION  
         [0002]    One common computer vision method is called “background-foreground segmentation,” or more simply “foreground segmentation.” In foreground segmentation, foreground objects are determined and highlighted in some manner. One technique for performing foreground segmentation is “background subtraction.”In this scheme, a camera views a background for a predetermined number of images, so that a computer vision system can “learn” the background. Once the background is learned, the computer vision system can then determine changes in the scene by comparing a new image with a representation of the background image. Differences between the two images represent a foreground object. A technique for background subtraction is found in A. Elgammal, D. Harwood, and L. Davis, “Non-parametric Model for Background Subtraction,” Lecture Notes in Comp. Science 1843, 751-767 (2000), the disclosure of which is hereby incorporated by reference.  
           [0003]    The foreground object may be described in a number of ways. Generally, a binary technique is used to describe the foreground object. In this technique, pixels assigned to the background are marked as black, while pixels assigned to the foreground are marked as white, or vice versa. Grey scale images may also be used, as can color images. Regardless of the nature of the technique, the foreground objects are marked such that they are distinguishable from the background. When marked as such, the foreground objects tend to look like “blobs,” in the sense that it is hard to determine what the foreground object is.  
           [0004]    Nevertheless, these foreground-segmented images can be further analyzed. One analysis tool used on these types of images is called connected components labeling. This tool scans images in order to determine “connected” pixel regions, which are regions of adjacent pixels that share the same set of intensity values. These tools undertake a variety of processes in order to determine how pixels should be grouped together. These tools are discussed, for example, in D. Vernon. “Machine Vision,” Prentice-Hall, 34-36 (1991) and E. Davies, “Machine Vision: Theory, Algorithms and Practicalities,” Academic Press, Chap. 6 (1990), the disclosures of which are hereby incorporated by reference. These and similar tools may be used, for example, to track objects that are passing into, out of, or through a camera view.  
           [0005]    While the connected component techniques and other blob-based techniques are practical and useful, there are, however, problems with these techniques. In general, these techniques (1) fail in the presence of noise, (2) treat individual parts of a scene independently, and (3) do not provide means to automatically count the number of blobs present in the scene. A need therefore exists for techniques that overcome these problems while providing adequate analysis of foreground-segmented images.  
         SUMMARY OF THE INVENTION  
         [0006]    Generally, techniques for analyzing foreground-segmented images are disclosed. The techniques allow clusters to be determined from the foreground-segmented images. New clusters may be added, old clusters removed, and current clusters tracked. A probabilistic framework is used for the analysis of the present invention.  
           [0007]    In one aspect of the invention, a method is disclosed that estimates cluster parameters for one or more clusters determined from an image comprising segmented areas, and evaluates the cluster or clusters in order to determine whether to modify the cluster or clusters. These steps are generally performed until one or more convergence criteria are met. Additionally, clusters can be added, removed, or split during this process.  
           [0008]    In another aspect of the invention, clusters are tracked during a series of images, such as from a video camera. In yet another aspect of the invention, predictions of cluster movements are made.  
           [0009]    In a further aspect of the invention, a system is disclosed that analyzes input images and creates blob information from the input images. The blob information can comprise tracking information, location information, and size information for each blob, and also can comprise the number of blobs present.  
           [0010]    A more complete understanding of the present invention, as well as further features and advantages of the present invention, will be obtained by reference to the following detailed description and drawings. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0011]    [0011]FIG. 1 illustrates an exemplary computer vision system operating in accordance with a preferred embodiment of the invention;  
         [0012]    [0012]FIG. 2 is an exemplary sequence of images illustrating the cluster detection techniques of the present invention;  
         [0013]    [0013]FIG. 3 is a flow chart describing an exemplary method for initial cluster detection, in accordance with a preferred embodiment of the invention;  
         [0014]    [0014]FIG. 4 is a flow chart describing an exemplary method for general cluster tracking, in accordance with a preferred embodiment of the invention; and  
         [0015]    [0015]FIG. 5 is a flow chart describing an exemplary method for specific cluster tracking, used for instance on an overhead camera that views a room, in accordance with a preferred embodiment of the invention. 
     
    
     DETAILED DESCRIPTION  
       [0016]    The present invention discloses a system and method for blob-based analysis. The techniques disclosed herein use a probabilistic framework and an iterative process to determine the number, location, and size of blobs in an image. A blob is a number of pixels that are highlighted in an image. Generally, the highlighting occurs through background-foreground segmentation, which is called “foreground segmentation” herein. Clusters are pixels that are grouped together, where the grouping is defined by a shape that is determined to fit a particular group of pixels. Herein, the term “cluster” is used to mean both the shape that is determined to fit a particular group of pixels and the pixels themselves. It should be noted, as shown in more detail in reference to FIG. 2, that one blob may be assigned to multiple clusters and multiple blobs may be assigned to one cluster.  
         [0017]    The present invention can also add, remove, and delete clusters. Additionally, clusters can be independently tracked, and tracking information can be output.  
         [0018]    Referring now to FIG. 1, a computer vision system  100  is shown interacting with input images  110 , a network, and a Digital Versatile Disk (DVD)  180 , and, in this example, producing blob information  170 . Computer vision system  100  comprises a processor  120  and a memory  130 . Memory  130  comprises a foreground segmentation process  140 , segmented images  150 , and a blob-based analysis process  160 .  
         [0019]    Input images  110  generally are a series of images from a digital camera or other digital video input device. Additionally, analog cameras connected to a digital frame-grabber may be used. Foreground segmentation process  140  segments input images  110  into segmented images  150 . Segmented images  150  are representations of images  110  and contain areas that are segmented. There are a variety of techniques, well known to those skilled in the art, for foreground segmentation of images. One such technique, as described above, is background subtraction. As is also described above, a technique for background subtraction is disclosed in “Non-parametric Model for Background Subtraction,” the disclosure of which is incorporated by reference above. Another technique that may be used is examining the image for skin tone. Human skin can be found through various techniques, such as the techniques described in Forsyth and Fleck, “Identifying Nude Pictures,” Proc. of the Third IEEE Workshop, Appl. of Computer Vision, 103-108, Dec. 2-4, 1996, the disclosure of which is hereby incorporated by reference.  
         [0020]    Once areas are found that should be segmented, the segmented areas are marked differently from other areas of the image. For instance, one technique for representing segmented images is through binary images, in which foreground pixels are marked white while background pixels are marked black or vice versa. Other representations include grey scale images, and there are even representations where color is used. Whatever the representation, what is important is that there is some demarcation to indicate a segmented region of an image.  
         [0021]    Once segmented images  150  are determined, then the blob-based analysis process  160  is used to analyze the segmented images  150 . Blob-based analysis process  160  uses all or some of the methods disclosed in FIGS. 3 through 5 to analyze segmented images  150 . The blob-based analysis process  160  examines the input images  110  and can create blob information  170 . Blob information  170  provides, for instance, tracking information of blobs, location of blobs, size of blobs, and number of blobs. It should also be noted that blob-based analysis process  160  need not output blob information  170 . Instead, blob-based analysis process  160  could output an alarm signal, for instance, if a person walks into a restricted area.  
         [0022]    The computer vision system  100  may be embodied as any computing device, such as a personal computer or workstation, containing a processor  120 , such as a central processing unit (CPU), and memory  130 , such as Random Access Memory (RAM) and Read-Only Memory (ROM). In an alternate embodiment, the computer vision system  100  disclosed herein can be implemented as an application specific integrated circuit (ASIC), for example, as part of a video processing system.  
         [0023]    As is known in the art, the methods and apparatus discussed herein may be distributed as an article of manufacture that itself comprises a computer-readable medium having computer-readable code means embodied thereon. The computer-readable program code means is operable, in conjunction with a computer system, to carry out all or some of the steps to perform the methods or create the apparatuses discussed herein. The computer readable medium may be a recordable medium (e.g., floppy disks, hard drives, compact disks such as DVD  180 , or memory cards) or may be a transmission medium (e.g., a network comprising fiber-optics, the world-wide web, cables, or a wireless channel using time-division multiple access, code-division multiple access, or other radio-frequency channel). Any medium known or developed that can store information suitable for use with a computer system may be used. The computer-readable code means is any mechanism for allowing a computer to read instructions and data, such as magnetic variations on a magnetic media or height variations on the surface of a compact disk, such as DVD  180 .  
         [0024]    Memory  130  will configure the processor  120  to implement the methods, steps, and functions disclosed herein. The memory  130  could be distributed or local and the processor  120  could be distributed or singular. The memory  130  could be implemented as an electrical, magnetic or optical memory, or any combination of these or other types of storage devices. The term “memory” should be construed broadly enough to encompass any information able to be read from or written to an address in the addressable space accessed by processor  120 . With this definition, information on a network is still within memory  130  of the computer vision system  100  because the processor  120  can retrieve the information from the network.  
         [0025]    [0025]FIG. 2 is an exemplary sequence of images illustrating the cluster detection techniques of the present invention. In FIG. 2, four image representations  201 ,  205 ,  235 , and  255  are shown. Each image representation illustrates how the present invention creates clusters in a still image  203 . Image  203  is one image from a digital camera. Note that still images are being used for simplicity. A benefit of the present invention is the ease at which the present invention can track objects in images. However, still images are easier to describe. It should also be noted that the process being described in reference to FIG. 2 is basically the method of FIG. 3. FIG. 2 is described before FIG. 3 because FIG. 2 is more visual and easier to understand.  
         [0026]    In image  203 , there are two blobs  205  and  210 , as shown by image representation  201 . In image representation  201 , it can be seen that a blob-based analysis process, such as blob-based analysis process  160 , has added a coordinate system to the image representation  205 . This coordinate system comprises X-axis  215  and Y-axis  220 . The coordinate system is used to determine locations of clusters and blobs contained therein, and to also provide further information, such as tracking information. Additionally, all blobs have been encircled with ellipse  230 , which has its own center  231  and axes  232  and  233 . Ellipse  230  is a representation of a cluster of pixels and is also a cluster.  
         [0027]    Through the steps of estimating cluster parameters for the ellipse  230  and evaluating the cluster  230 , the present invention refines the image representation into the image representation  235 . In this representation, two ellipses are chosen to represent blobs  205  and  210 . Ellipse  240 , which has a center  241  and axes  242  and  243 , represents blob  205 . Meanwhile, ellipse  250 , which has a center  251  and axes  252  and  253 , represents blob  210 .  
         [0028]    After another iteration, the present invention might determine that image representation  255  is the best representation of image  203 . In image representation  255 , blob  210  is further represented by ellipse  260 , which has a center  261  and axes  262  and  263 . Blob  210  is represented by ellipses  270  and  280 , which have centers  271 ,  281  and axes  272 ,  282  and  273 ,  283 , respectively.  
         [0029]    Thus, the present invention has determined, in the example of FIG. 2, that there are three clusters. However, these clusters may or may not actually represent three separate entities, such as individuals. If the present invention is used to track clusters, additional steps will likely be needed to observe how the blobs move over a series of images.  
         [0030]    Before describing the methods of the present invention, it is also helpful to describe how segmented images may be modeled through a probabilistic framework. The described algorithms of FIGS. 3 through 5 use parametric probability models to represent foreground observations. A fundamental assumption is that the representation of these observations with a reduced number of parameters facilitates the analysis and understanding of the information captured in the images. Additionally, the nature of the statistical analysis of the observed data provides reasonable robustness against errors and noise present in real-life data.  
         [0031]    In this probabilistic framework, it is beneficial to use two-dimensional (2D) random processes, X=(x,y) ε           2 , associated with the positions in which foreground pixels are expected to be observed on foreground segmentation images. As a result, the information contained on a set of pixels of a binary image can then be captured by the parameters of the probability distribution of the corresponding random process. For example, a region that depicts the silhouette of an object in an image can be represented by a set of parameters that capture the location and shape of the object.  
         [0032]    Binary images, which are two-dimensional arrays of binary pixel values, can be represented with the collection of pixels with non-zero values (i.e., the foreground, in the case of many foreground segmentation methods) through the following equation:  
         Image={ X   k   I ( X   i )≠0}.  [1] 
         [0033]    This collection of pixels can be interpreted as observation samples drawn from a two-dimensional random process with some parameterized probability distribution P(X|θ).  
         [0034]    Under this representation, random processes can be used to model foreground objects observed in a scene as well as the uncertainties in these observations, such as noise and shape deformations. For example, the image of a sphere can be represented as a cluster of pixels described by a 2D-Gaussian distribution, P(X|θ)=N(X; X 0 ,Σ), in which the mean X 0  provides location of its center, and the covariance Σ captures information about its size and shape.  
         [0035]    Complex objects can be represented with multiple clusters that might or might not be connected to each other. These complex random processes can be written as:  
               P        (   X   )       =       ∑     k   =   1     M                       P        (     X   |     θ   k       )              P        (     θ   k     )       .                 [   2   ]                               
 
         [0036]    Note that, given the probability distribution of the foreground pixels, one can reconstruct an approximation of the image by giving non-zero values to all the pixel positions with probability greater that some threshold, and zero values to the rest of the pixels. However, the problem of greatest relevance is that of analyzing the images to obtain the probability models.  
         [0037]    The analysis of the input image is then turned into the problem of estimating the parameters of a model by fitting it to the observation samples given by the image. That is, given a binary-segmented image, an algorithm determines the number of clusters and the parameters of each cluster that best describes the non-zero pixels in the image, where the non-zero pixels are the foreground objects.  
         [0038]    The methods of the present invention are described in the following manner: (1) FIG. 3 describes an initial cluster detection method, which determines clusters from an image; (2) FIG. 4 describes a general cluster tracking method, which is used to track objects over several or many images; and (3) FIG. 5 describes a specialized cluster tracking method, suitable for situations involving, for instance, tracking and counting objects from an camera viewpoint that points down into a room.  
         [0039]    Initial Cluster Detection  
         [0040]    [0040]FIG. 3 is a flow chart describing an exemplary method  300  for initial cluster detection, in accordance with a preferred embodiment of the invention. Method  300  is used by a blob-based analysis process to determine blob information, and method  300  accepts a segmented image for analysis.  
         [0041]    Method  300  basically comprises three major steps: initializing  305 , estimating cluster parameters  310 , and evaluating cluster parameters  330 .  
         [0042]    Method  300  begins in step  305 , when the method initializes. For method  300 , this step entails starting with a single ellipse covering the whole image, as shown by image representation  205  of FIG. 2.  
         [0043]    In step  310 , cluster parameters are estimated. Step  310  is a version of the Expectation-Maximization (EM) algorithm, which is described in more detail in A. Dempster, N. Laird, and D. Rubin, “Maximum Likelihood From Incomplete Data via the EM Algorithm,” J. Roy. Statist. Soc. B 39:1-38 (1977), the disclosure of which is hereby incorporated by reference. In step  315 , pixels belonging to foreground segmented portions of an image are assigned to current clusters. For brevity, “pixels belonging to foreground segmented portions of an image” are entitled “foreground pixels” herein. Initially, this means that all foreground pixels are assigned to one cluster.  
         [0044]    In step  315 , each foreground pixel is assigned to the closest ellipse. Consequently, pixel X is assigned to the ellipse θ k  such that P(X|θ k ) is maximized.  
         [0045]    In step  320 , the cluster parameters are re-estimated based on the pixels assigned to each cluster. This step estimates the parameters of each θ k  to best fit the foreground pixels assigned to this cluster, θ k .  
         [0046]    In step  325 , a test for convergence is performed. If converged (step  325 =YES), step  325  is finished. Otherwise (step  325 =NO), the method  300  starts again at step  315 .  
         [0047]    To test for convergence, the following steps are performed. For each cluster θ k , measure how much the cluster has changed in the last iteration. To measure change, one can use changes in position, size, and orientation. If the changes are small, beneath a predetermined value, the cluster is marked as converged. Overall convergence is achieved when all clusters are marked as converged.  
         [0048]    It should be noted that step  325  can also test for a maximum number of iterations. If the maximum number of iterations is reached, the method  300  continues to step  330 .  
         [0049]    In step  330 , the clusters are evaluated. In this step, the clusters may be split or deleted if certain conditions are met. In step  335 , a particular cluster is selected. In step  340 , it is determined if the selected cluster should be deleted. A cluster is deleted (step  340 =YES and step  345 ) if no or very few pixels are assigned to it. Thus, if there are less than a predetermined number of pixels assigned to the cluster, the cluster is deleted (step  340 =YES and step  345 ). If the cluster is deleted, the method continues in step  360 , else the method continues in step  350 .  
         [0050]    In step  350 , it is determined if the selected cluster should be split. A cluster is split (step  350 =YES and step  355 ) if the split condition is satisfied. To evaluate the split condition, the method  300  considers all the pixels assigned to the cluster. For each pixel, evaluate the distance (X−X 0 ) T π −1 (X−X 0 ), in which the mean X 0  provides location of the center of the ellipse, and the covariance Σ captures information about its size and shape. The outline of the ellipse is the points with distance D 0 , typically D 0 =3*3=9. The “inside points” are pixels with distances, for example, smaller than 0.25*D 0  and the “outside points” are pixels with distances, for example, larger than 0.75*D 0 . Compute the ratio of the number of outside points divided by the number of inside points. If this ratio is larger than a threshold, the ellipse is split (step  355 ).  
         [0051]    In step  360 , it is determined if there are more clusters. If there are additional clusters (step  360 =YES), then the method  300  again selects another cluster (step  335 ). If there are no more clusters, the method  300  continues at step  370 .  
         [0052]    Step  370  performs one or more tests for convergence. First, in step  370 , a determination is made as to whether the method is converged. The test for convergence is the same used in step  325 , which is as follows. For each cluster ok, measure how much the cluster has changed in the last iteration. To measure change, one can use changes in position, size and orientation. If the changes are small, beneath a predetermined value, the cluster is marked as converged. Overall convergence is achieved when all clusters are marked as converged.  
         [0053]    If there is no convergence (step  370 =NO), then the method  300  continues again at step  315 . It should be noted that step  370  may also determine if a maximum number of iterations have been reached. If the maximum number of iterations have been reached, the method  300  continues in step  380 .  
         [0054]    If there is convergence (step  370 =YES) or, optionally, the maximum number of iterations is reached (step  370 =YES), then blob information is output in step  380 . The blob information can contain, for example, the locations, sizes, and orientations of all blobs, and also the number of blobs. Alternatively, as discussed previously, blob information need not be output. Instead, information such as a warning or alarm could be output. For instance, if a person enters a restricted area, then the method  300  can output an alarm signal in step  380 .  
         [0055]    It should be noted that method  300  may determine that there are no clusters suitable for tracking. For example, although not discussed above, clusters may be assigned a minimum dimension. If no cluster meets this dimension, then the image might be considered to have no clusters. This is also the case if there are no foreground segmented areas of an image.  
         [0056]    Thus, method  300  provides techniques for determining clusters in an image. Because a probabilistic framework is used, the present invention increases the robustness of the system against noise and errors in the foreground segmentation algorithms.  
         [0057]    General Cluster Tracking  
         [0058]    General cluster tracking is performed by the exemplary method  400  of FIG. 4. This algorithm assumes a sequence of images and uses the solution for each frame to initialize the estimation process for the next frame. In a typical tracking application, the method  400  starts with the initial cluster detection from the first frame and then proceeds with the cluster tracking for subsequent frames. Many of the steps in method  400  are the same as the steps in method  300 . Consequently, only differences will be described herein.  
         [0059]    In step  410 , the method initializes by the solution obtained in the previous image frame. This provides the current iteration of method  400  with the results of the previous iteration of method  400 .  
         [0060]    Parameters of clusters are estimated in step  310  as discussed above. This step generally modifies the cluster to track movement of blobs between images.  
         [0061]    The step of evaluating clusters, step  430 , remains basically the same. For instance, the method  400  can delete clusters (step  340  and  345 ) and split clusters (steps  350  and  355 ) as in the previous algorithm  300 . However, new clusters may be added for data that was not described by the initial solution. In step  425 , a determination as to whether a new cluster should be added is made. If a new cluster should be added (step  425 =YES), a new cluster is created and all pixels not assigned to the existing clusters are assigned to the new cluster (step  428 ). Subsequent iterations will then refine, and split if necessary, this newly added cluster. The additional cluster typically occurs when a new object enters the scene.  
         [0062]    Specialized Cluster Tracking  
         [0063]    [0063]FIG. 5 is a flow chart describing an exemplary method  500  for specific cluster tracking, used for instance on an overhead camera that views a room. In this section, exemplary specific modifications are explained that are used for overhead camera tracking and people counting. The overall scheme is the same as described above, so only differences will be described here.  
         [0064]    In step  410 , the system is initialized by the solution determined through the previous image frame. However, for each ellipse, the previous motion of an ellipse is used to predict its position in the current iteration. This occurs in step 510. The size and orientation of the predicted ellipse are kept the same, although changes to the size and orientation of the ellipse can be predicted, if desired. The center position is predicted based on previous center positions. For this prediction, a Kalman filter may be used. A reference that describes Kalman filtering is “Applied Optimal Estimation,” Arthur Gelb (Ed.), MIT Press, chapter 4.2 (1974), the disclosure of which is hereby incorporated by reference. Prediction may also be performed through simple linear prediction, as follows:  
           P   x     0   ( t +1)= x   0 ( t )+( x   0 ( t )−X 0 ( t− 1)),  [3] 
         [0065]    where P x     0   (t+1) is the predicted center at time t+1, and X 0 (t) and x 0 (t−1) are the centers at times t and t−1, respectively.  
         [0066]    The step of estimating cluster parameters, step  310 , remains basically the same. For real-time video processing with frame rates such as 10 frames per second, it is possible to only perform one or two iterations of each loop, because the tracked objects change slowly.  
         [0067]    The step of evaluating clusters ( 530 ) remains basically unchanged. The addition of new clusters (step  425  of FIG. 4) is, however, modified in method  500 . In particular, if it is determined that a new cluster needs to be added (step  425 =YES), all the foreground pixels not assigned to the current clusters are examined. However, instead of assigning all those pixels to a single new cluster, the connected components algorithm is performed on the unassigned pixels (step  528 ), and one or more new clusters are created for each connected component (step  528 ). This is beneficial when multiple objects appear at the same time in different parts of the image, as the connected component algorithm will determine whether blobs are connected in a probabilistic sense. Connected component algorithms are described in, for example, D. Vernon. “Machine Vision,” Prentice-Hall, 34-36 (1991) and E. Davies, “Machine Vision: Theory, Algorithms and Practicalities,” Academic Press, Chap. 6 (1990), the disclosures of which have already been incorporated by reference.  
         [0068]    The present invention has at least the following advantages: (1) the present invention improves performance by using global information from all the blobs to help in the parameter estimation of each individual one; (2) the present invention increases the robustness of the system against noise and errors in the foreground segmentation algorithms; and (3) the present invention automatically determines the number of blobs in a scene.  
         [0069]    While ellipses have been shown as being clusters, other shapes may be used.  
         [0070]    It is to be understood that the embodiments and variations shown and described herein are merely illustrative of the principles of this invention and that various modifications may be implemented by those skilled in the art without departing from the scope and spirit of the invention. Additionally, “whereby” clauses in the claims are to be considered non-limiting and merely for explanatory purposes.