Abstract:
The method facilitates efficient motion estimation for video sequences captured with a stationary camera with respect to an object. For video captured with this type of camera, a main cause of changes between adjacent frames corresponds to object motion. In this setting the output from the motion compensation stage is the block matching algorithm describing the way pixel blocks move between adjacent frames. For video captured with cameras mounted on moving vehicles (e.g. school buses, public transportation vehicles and police cars), the motion of the vehicle itself is the largest source of apparent motion in the captured video. In both cases, the encoded set of motion vectors is a good descriptor of apparent motion of objects within the field of view of the camera.

Description:
TECHNICAL FIELD 
     The presently disclosed embodiments are directed toward efficient motion compensation using video motion vector information within the process of video compression. However, it is to be appreciated that the present exemplary embodiments are also amenable to other like applications. 
     BACKGROUND 
     Block-based motion estimation is an important element in many video coding standards that aims at removing temporal redundancy between neighboring frames. Traditional methods for block-based motion estimation such as the Exhaustive Block Matching Algorithm (EBMA) are capable of achieving good matching performance but are computationally expensive. Alternatives to EBMA have been proposed to reduce the amount of search points by trading off matching optimality with computational resources. Although they exploit shared local spatial characteristics around the target block, they fail to take advantage of the spatio-temporal characteristics of the video data itself. Spatio-temporal characteristics of the video provide useful information that can reduce the computational load incurred by block-matching algorithms in cameras (e.g., mounted cameras for traffic monitoring in highways) where motion characteristics of objects have trending patterns across time. 
     Video compression is employed in applications where high quality video transmission and/or archival is required. For example, a surveillance system typically includes a set of cameras that relay video data to a central processing and archival facility. While the communication network used to transport the video stream between the cameras and the central facility may be built on top of proprietary technology, traffic management centers have recently started to migrate to Internet Protocol- or IP-compliant networks. In either case, the underlying communication network typically has bandwidth constraints which dictate the use of video compression techniques on the camera end, prior to transmission. In the case of legacy analog cameras, compression is performed at an external encoder attached to the camera, whereas digital or IP cameras typically integrate the encoder within the camera itself. Typical transmission rates over IP networks require the frame rate of multi-megapixel video streams to be limited to fewer than 5 frames per second (fps). The latest video compression standards enable the utilization of the full frame rate camera capabilities for transmitting high definition video at the same network bandwidth. For example, transmission of 1080 p HD uncompressed video requires a bandwidth of 1.5 Gbps, while its compressed counterpart requires only 250 Mbps; consequently, transmission of compressed video with at least 6 times the frame rate of the uncompressed version would be possible over the same network infrastructure. 
     Video compression is achieved by exploiting two types of redundancies within the video stream: spatial redundancies amongst neighboring pixels within a frame, and temporal redundancies between adjacent frames. This modus operandi gives raise to two different types of prediction, namely intra-frame and inter-frame prediction, which in turn result in two different types of encoded frames, reference and non-reference frames. Reference frames, or “I-frames” are encoded in a standalone manner (intra-frame) using compression methods similar to those used to compress digital images. Compression of non-reference frames (e.g., P-frames and B-frames) entails using inter-frame or motion-compensated prediction methods where the target frame is estimated or predicted from previously encoded frames in a process that typically entails three steps: (i) motion estimation, where motion vectors are estimated using previously encoded frames. The target frame is segmented into pixel blocks called target blocks, and an estimated or predicted frame is built by stitching together the blocks from previously encoded frames that best match the target blocks. Motion vectors describe the relative displacement between the location of the original blocks in the reference frames and their location in the predicted frame. While motion compensation of P-frames relies only on previous frames, previous and future frames are typically used to predict B-frames; (ii) residual calculation, where the error between the predicted and target frame is calculated; and (iii) compression, where the error residual and the extracted motion vectors are compressed and stored. Throughout the teachings herein, the terms “motion vector” and “compression-type motion vector” are used synonymously. 
     There is a need in the art for systems and methods that facilitate block-based motion estimation that are both computationally efficient and capable of exploiting the dominant spatio-temporal characteristics of the motion patterns captured in the video, without sacrificing matching performance relative to exhaustive methods, while overcoming the aforementioned deficiencies. 
     BRIEF DESCRIPTION 
     In one aspect, a computer-implemented method for performing motion estimation to compress video frames using at least one optimized search neighborhood, comprises segmenting a target frame into target pixel blocks, determining whether each of the one or more target blocks is a candidate for efficient motion compensation, and for those target blocks deemed to be candidates for efficient motion block estimation, optimizing the search neighborhood and performing a block matching search on the optimized search neighborhood. 
     In another aspect, a system that facilitates estimating motion for compression of video frames using at least one optimized search neighborhood comprises a camera that captures video of a moving object, and a processor configured to segment a target frame into target pixel blocks, determine whether each of the one or more target blocks is a candidate for efficient motion compensation, and, for those target blocks deemed to be candidates for efficient motion block estimation, optimizing the search neighborhood and performing a block-matching search on the optimized search neighborhood. 
     In yet another aspect, a non-transitory computer-readable medium stores computer-executable instructions for performing motion estimation for compression of video frames using at least one optimized search neighborhood, the instructions comprising segmenting a target frame into target pixel blocks, determining whether each of the one or more target blocks is a candidate for efficient motion compensation, and, for those target blocks deemed to be candidates for efficient motion block estimation, optimizing the search neighborhood and performing a block-matching search on the optimized search neighborhood. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a method for performing block-based motion estimation for video compression and learning the dominant spatio-temporal characteristics of the motion patterns within the scene being captured, in accordance with one or more features described herein. 
         FIG. 2  illustrates a high level overview of a system performing block-based motion estimation for video compression and learning the dominant spatio-temporal characteristics of the motion patterns within the scene being captured, in accordance with one or more features described herein. 
         FIG. 3A  shows a reference block and search neighborhood in reference frame. 
         FIG. 3B  shows a target block in a target frame. 
         FIG. 4A  shows a reference frame within a video sequence. 
         FIG. 4B  shows a target frame within a video sequence. 
         FIG. 4C  shows the corresponding motion vector field. 
         FIG. 5A  illustrates a sample 2D global histogram of motion vector components resulting from the aggregation of motion estimation data across video frames. 
         FIG. 5B  illustrates a histogram generated after removal of the (0,0) entry, such as is generated when updating the global histogram. 
         FIG. 6A  shows a sample video scene with two highlighted target motion blocks one in red and one in blue. 
         FIG. 6B  shows a 2D histogram of motion vector components corresponding to the blue motion block of  FIG. 6A . 
         FIG. 6C  shows another 2D histogram of motion vector components corresponding to the red motion block of  FIG. 6A . 
         FIG. 7A  illustrates a histogram to which a thresholding algorithm has been applied when determining whether a specific block or group of blocks is a candidate for efficient motion compression. 
         FIG. 7B  illustrates another histogram to which a thresholding algorithm has been applied when determining whether a specific block or group of blocks of is a candidate for efficient motion compression. 
         FIG. 8A  illustrates a histogram that facilitates determining predominant motion directions and reduced search zones. 
         FIG. 8B  illustrates another histogram that facilitates determining predominant motion directions and reduced search zones. 
         FIG. 8C  illustrates another histogram that facilitates determining predominant motion directions and reduced search zones. 
         FIG. 9  illustrates a system that facilitates efficient motion compensation, in accordance with one or more aspects described herein. 
     
    
    
     DETAILED DESCRIPTION 
     The above-described problem is solved by performing block-based motion estimation for video compression that is capable of learning the dominant spatio-temporal characteristics of the motion vector patterns within the scene being captured. It is particularly well suited for stationary traffic cameras that monitor roads and highways for traffic law enforcement purposes, as well as for vehicle-mounted cameras (e.g. cameras mounted on school buses, public transportation vehicles and police cars). It relies on learning predominant motion characteristics of objects within the field of view of the camera and reduces the block-matching search space by adaptively changing the search neighborhood size and orientation based on the learned historical motion characteristics of the video. Alternatively, the predominant motion characteristics can be manually input by a camera operator. The learning approach includes a motion-learning phase that builds global and block-based histograms of historical motion behavior, and a block-based motion estimation phase that performs searches in reduced or optimized neighborhoods of the target block according to the learnt historical motion behavior patterns. Learning the historical patterns of behavior of global and local motion vectors enables the described algorithm to achieve considerable savings in computation at a reduced cost in matching performance with respect to multi-stage block-matching algorithms. Predominant motion characteristics can also be input manually by an operator during camera installation, maintenance or monitoring. 
       FIG. 1  illustrates a method for performing block-based motion estimation for video compression and learning the dominant spatio-temporal characteristics of the motion patterns within the scene being captured, in accordance with one or more features described herein. At  10 , video is acquired using, for example, a traffic surveillance camera (e.g., a stationary camera, a camera mounted on a vehicle, or any other suitable camera). For instance, a conventional traffic camera or other video camera may be used for capturing live video. Additionally or alternatively Internet protocol (IP) cameras may also be employed, which perform embedded video compression prior to transmission. Alternatively, the compressed video may be available from a video database where video sequences from surveillance cameras are stored. 
     At  11 , a target frame n is segmented into a plurality of target pixel blocks. At  12 , the target frame n is read and motion vectors associated with the compression of the frame (if non-reference) are determined or computed via traditional motion estimation algorithms. At  14 , a global 2D histogram H n (dx,dy) of motion vector components is updated along the horizontal (dx) and vertical (dy) directions based on H n−1 (dx,dy) and the histogram at frame n, h n (dx,dy) via an auto-regressive moving average computation such that:
 
 H   n ( dx,dy )=α h   n ( dx,dy )+(1−α)H n−1 ( dx,dy )
 
where α is a constant such that 0≦α1. When h n (dx,dy) is smooth (thus indicating the absence of predominant motion patterns in the scene), α can be set to 0 so as to exclude frames when no motion is observed in the scene from histogram calculation. If the histogram is mostly uniform, the method reverts to  11  and a next frame is segmented, otherwise the method proceeds to  16 . Step  14  is a preprocessing step for the next level where the histograms for local regions are calculated.
 
     At  16 , at frame n and for every target block (or target block group) k, a local 2D histogram H nk (dx,dy) of the motion vector components dx and dy based on H n−1k (dx,dy) and h nk (dx,dy) is updated via an auto-regressive moving average computation such that:
 
 H   nk ( dx,dy )=α h   nk ( dx,dy )+(1−α)H n−1k ( dx,dy )
 
where α is a constant such that 0≦α≦1. If n≧N where N is a predetermined threshold that determines the length of the learning period, and the histogram is clustered, a block (or block group) k is labeled as a candidate for efficient motion compensation, and the method reverts to  11  for additional frame analysis until all or a subset of all frames have been analyzed. It will be appreciated that in other embodiments, candidacy for efficient motion compensation may be determined manually and/or via a priori labeling of candidate target blocks.
 
     The efficient motion estimation phase begins at  18 , where, for every block (or block group) in the target frame, a determination is made whether the block or block group is a candidate for efficient motion compensation from the labels assigned by the learning stage (steps  11 ,  12 ,  14 ,  16 ). If not, then a traditional block matching algorithm can be performed and, optionally, histogram monitoring can be continued to detect a presence of predominant motion characteristics. In one example, initial search neighborhood attributes (e.g., size, orientation, etc.) are learned or computed as a function of the determined predominant motion vector directions. In another example initial search neighborhood attributes are manually input by an operator or technician. If the block or block group is a candidate for efficient motion compensation, then at  20  the local 2D histogram of motion vector components of the target block (or block group) processed and predominant motion vector directions are determined. Search neighborhood size and orientation (e.g., search direction) is modified to perform block matching only along predominant directions, at  22 . That is, a block-matching search is executed on a modified or optimized search neighborhood, such that for a candidate block, a search for a matching block in subsequent video frames is performed along the determined predominant motion vector directions and magnitudes. The method then reverts to  11  for subsequent iteration on a new frame. 
     In another embodiment, block labels can be assigned by a camera operator by segmenting regions with dominant motion characteristics (e.g. highway lanes, road shoulders, areas on the side of the road) from those without such characteristics (e.g. sidewalks with unstructured pedestrian traffic). Once the manual labeling process is complete, the algorithm can then proceed to learn dominant motion characteristics (e.g. direction of motion along a specific road, or lack of motion on the side of the road). Alternatively, these dominant patterns can also be manually input by a camera operator, for example by pointing out the predominant direction and speed of traffic on a road lane. 
     In one example, search neighborhood attributes (e.g. size, orientation, etc.) are adjusted as a function of time. For instance, a search neighborhood may be smaller (e.g., five pixels) during peak traffic times (e.g. when traffic is expected to be slow and motion of a given vehicle is slower), and larger (e.g., 10 pixels) during off-peak traffic times (e.g. when traffic is expected to move rapidly and vehicle motion between frames is greater.) 
     In another example, search neighborhood attributes (e.g. size, orientation, etc.) are adjusted as a function of traffic conditions independent of time of day. For instance, a search neighborhood may be smaller (e.g., five pixels) for slow-moving traffic, and larger (e.g., 10 pixels) for fast-moving traffic. 
     It will be appreciated that the method of  FIG. 1  can be implemented by a computer  30 , which comprises a processor (such as the processor  204  of  FIG. 9 ) that executes, and a memory (such as the memory  206  of  FIG. 9 ) that stores, computer-executable instructions for providing the various functions, etc., described herein. 
     The computer  30  can be employed as one possible hardware configuration to support the systems and methods described herein. It is to be appreciated that although a standalone architecture is illustrated, that any suitable computing environment can be employed in accordance with the present embodiments. For example, computing architectures including, but not limited to, stand alone, multiprocessor, distributed, client/server, minicomputer, mainframe, supercomputer, digital and analog can be employed in accordance with the present embodiment. In one embodiment, the herein described processing is performed in the camera  31  ( FIG. 2 ) and/or camera  202  ( FIG. 9 ). 
     The computer  30  can include a processing unit (see, e.g.,  FIG. 9 ), a system memory (see, e.g.,  FIG. 9 ), and a system bus (not shown) that couples various system components including the system memory to the processing unit. The processing unit can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures also can be used as the processing unit. 
     The computer  30  typically includes at least some form of computer readable media. Computer readable media can be any available media that can be accessed by the computer. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. 
     Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. 
     A user may enter commands and information into the computer through an input device (not shown) such as a keyboard, a pointing device, such as a mouse, stylus, voice input, or graphical tablet. The computer  30  can operate in a networked environment using logical and/or physical connections to one or more remote computers, such as a remote computer(s). The logical connections depicted include a local area network (LAN) and a wide area network (WAN). Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
       FIG. 2  illustrates a high level overview of a system for performing the method of  FIG. 1 , in accordance with one or more aspects described herein. The system facilitates a learning phase during which global and local (block-based) histograms of historical motion behavior are built or generated, and a motion estimation phase during which searches are performed in reduced or optimized neighborhoods of a target block according to the learned historical motion behavior patterns. 
     According to  FIG. 2 , a traffic surveillance camera  31  (or other suitable video recording device) acquires video of passing vehicles. The system further includes a motion vector calculation module  32  that determines motion vectors from the incoming live uncompressed video stream, wherein the motion vectors are of a type used for video compression. 
     The system further includes a motion pattern learning module  33  that performs various actions, including but not limited to those described with regard to steps  11 ,  12 ,  14 , and  16  of  FIG. 1 . A frame reconstruction and entropy encoding module  34  reconstructs and encodes frames of the captured video to generate compressed video data. 
     In other embodiments, other types of camera that capture video where the predominant direction of motion can easily be extracted and learned across time may include vehicle mounted cameras (e.g. school bus, public transportation vehicles and police car dashboard cameras), in which the dominant motion characteristics are determined by the location of the camera on the vehicle as well as the motion of the vehicle itself. For example, the apparent motion captured by a side-mounted camera on a school bus will predominantly be from right to left (left to right) if the camera is mounted on the left (right) hand side of the bus. Similarly, for dashboard cameras facing forward, the predominant motion pattern will be located along radial directions originating at the so-called focus of expansion. 
       FIGS. 3A and 3B  depict a graphical description of a block matching algorithm.  FIG. 3A  shows a reference block  40  and search neighborhood  41  in reference frame  42 .  FIG. 3B  shows a target block  43  in a target frame  44 . A block matching algorithm or module executed by processor breaks up the frame to be compressed (the target frame) into pixel blocks of a predetermined size. The size of a motion block may be denoted by m×n pixels, where typically m=n=16 pixels. A search is performed by the processor in the reference frame for the block that is most similar to the current m×n target pixel block. Since searching and calculating similarity metrics is a computationally expensive process, a search neighborhood is typically defined around the location of the target motion block as shown in  FIG. 3A . Examples of similarity criteria between the blocks are, e.g., the mean squared error (MSE) and the mean absolute difference (MAD), which are calculated as: 
                     MSE   ⁡     (       d   1     ,     d   2     ,   j     )       =       (     1   mn     )     ⁢     ∑       (       B   ⁡     (     k   ,   l   ,   j     )       -     B   ⁡     (       k   +     d   1       ,     l   +     d   2       ,     j   -   1       )         )     2                 (   1   )                 MAD   ⁡     (       d   1     ,     d   2     ,   j     )       =       (     1   mn     )     ⁢     ∑            B   ⁡     (     k   ,   l   ,   j     )       -     B   ⁡     (       k   +     d   1       ,     l   +     d   2       ,     j   -   1       )                          (   2   )               
where B(k,l,j) denotes the pixel located on the k-th row and I-th column of the m&#39;n block of pixels in the j-th frame. In this case, the (j−1)-th frame is the already encoded frame being used as a reference frame, and the j-th frame is the target frame. Since both MSE and MAD measure how dissimilar two blocks are, a block similarity measure can then be defined as the reciprocal or the negative MSE or MAD. The motion vector for the target pixel block is the vector (d 1 ,d 2 ) that maximizes the block similarity measure between the target and reference blocks.
 
       FIGS. 4A and 4B  show two 1728×2304 pixel adjacent frames: a reference frame  60  ( FIG. 4A ) and a target frame  62  ( FIG. 4B ) within a video sequence.  FIG. 4C  shows the corresponding 16×16 pixel motion vector field  64 . The motion vector cluster  65  is a representation of motion of the vehicle in the image frame of  FIG. 4A . 
       FIG. 5A  illustrates a sample 2D global histogram  80  of motion vector components resulting from the aggregation of motion estimation data across  30  video frames.  FIG. 5B  illustrates a histogram  90  generated from the histogram  80  ( FIG. 5A ) after removal of the (0,0) entry, such as is generated when updating the global histogram at  14  ( FIG. 1 ). The histogram in  FIG. 5A  has a large peak at dx=0 and dy=0 (the location that corresponds to stationary blocks), implying that most of the blocks in the image remain stationary for the 30 frames analyzed.  FIG. 5B  shows the histogram from  FIG. 5A  where the histogram entry at dx=0 and dy=0 has been removed for easier visualization of non-zero motion block frequency. It can be seen that a significant majority of the motion vectors associated with non-stationary pixels are located along a diagonal region that corresponds to the direction of the traffic in the acquired video. 
       FIGS. 6A-6C  illustrate an image frame and corresponding histograms such as are generated at  16  ( FIG. 1 ). The presence of peaks in the global histogram, such as in the one shown in  FIG. 5 , indicates the existence of predominant motion patterns in the video sequence. Computation of block or block group 2D histograms aids the identification of scene regions with predominant motion patterns.  FIG. 6A  shows a sample video scene  98  with two highlighted target motion blocks  100 ,  102 , one in red and one in blue.  FIGS. 6B and 6C  show the 2D histograms  110 ,  120 , respectively, of the motion vector components of the regions highlighted by each of the blocks  100 ,  102 . From inspection of  FIGS. 6B and 6C , it can be seen that the motion blocks in the region highlighted by the blue outline  100  are mainly stationary (in the absence of moving vehicles) or oriented along the direction of the traffic flow, while the region highlighted by the red outline  102  is mostly stationary. 
       FIGS. 7A and 7B  illustrate histograms  130 ,  140 , respectively, to which a thresholding algorithm has been applied, such as is described with regard to  18  ( FIG. 1 ) when determining whether a specific block or group of blocks is a candidate for efficient motion compression. Candidates for efficient motion estimation can be found, for example, by performing thresholding on the histogram data (so as to eliminate the influence of outliers or fictitious motion vectors) and limiting the search regions to locations where peaks are still present after the thresholding. The value of the threshold can be used to control the degree of simplification of the search areas; for example, more aggressive thresholding will lead to a more drastic reduction in the size of the search area, and vice-versa. Other clustering and segmentation techniques can be used to determine dominant patterns of motion from the histogram data. In one example, the histograms  130 ,  140  represent the histograms from  FIGS. 6A and 6B  after a thresholding operation, where the threshold has been defined as a fraction (e.g., a factor of 0.6 or some other threshold factor) of the histogram entry at (0,0). 
       FIGS. 8A-8C  illustrate histograms that facilitate determining predominant motion directions and reduced search zones as described with regard to  20  ( FIG. 1 ). The locations of the histogram peaks that remain after the thresholding operation indicate predominant motion direction and magnitude at the specific block or region. This facilitates detecting and exploiting the characteristics of any predominant motion patterns that are present in the scene and limit the motion compensation search regions accordingly. In the example illustrated by  FIG. 7A , which is associated with blocks located along the highway, the search region for these blocks would be limited to locations along the direction of motion of the traffic, as highlighted by the red outline in  FIG. 8A . Green and yellow outlines highlighting the search regions for traditional exhaustive algorithms (±8 and ±16 pixel neighborhoods, respectively) are also included. It can be seen that the proposed search region is considerably smaller than either of the regions that are traditionally used in exhaustive algorithms. Additional computational savings would be attained if traffic moves at an approximately constant speed across the field of view, as this would constrain the radius or magnitude of search in addition to the direction of search, as illustrated by the red outline in  FIG. 8B . In the case of predominantly stationary pixels, the search region would be centered at the origin, as highlighted by the red outline in  FIG. 8C . 
       FIG. 9  illustrates a system  200  that facilitates efficient motion compensation, in accordance with one or more aspects described herein. The system is configured to perform the method(s), techniques, etc., described herein with regard to the preceding figures, and comprises a camera  202 , which is coupled to a processor  204  that executes, and a memory  206  that stores computer-executable instructions for performing the various functions, methods, techniques, steps, and the like described herein. The camera  202  may be a stationary traffic surveillance camera, or a camera mounted on a vehicle such as a police cruiser or emergency response vehicle, or any other suitable camera for recording video. The processor  204  and memory  206  may be integral to each other or remote but operably coupled to each other. In another embodiment, the processor and memory reside in a computer (e.g., the computer  30  of  FIG. 1 ) that is operably coupled to the camera  202 . 
     As stated above, the system  200  comprises the processor  204  that executes, and the memory  206  that stores one or more computer-executable modules (e.g., programs, computer-executable instructions, etc.) for performing the various functions, methods, procedures, etc., described herein. “Module,” as used herein, denotes a set of computer-executable instructions, software code, program, routine, or other computer-executable means for performing the described function, or the like, as will be understood by those of skill in the art. Additionally, or alternatively, one or more of the functions described with regard to the modules herein may be performed manually. 
     The memory may be a computer-readable medium on which a control program is stored, such as a disk, hard drive, or the like. Common forms of non-transitory computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tape, or any other magnetic storage medium, CD-ROM, DVD, or any other optical medium, RAM, ROM, PROM, EPROM, FLASH-EPROM, variants thereof, other memory chip or cartridge, or any other tangible medium from which the processor can read and execute. In this context, the systems described herein may be implemented on or as one or more general purpose computers, special purpose computer(s), a programmed microprocessor or microcontroller and peripheral integrated circuit elements, an ASIC or other integrated circuit, a digital signal processor, a hardwired electronic or logic circuit such as a discrete element circuit, a programmable logic device such as a PLD, PLA, FPGA, Graphical card CPU (GPU), or PAL, or the like. 
     Recorded video  208  captured by the camera  202  stored in the memory  26 . Additionally or alternatively, video that has been captured and compressed by the camera  202  can be received and stored in the memory  206  for analysis by the processor  204 . For instance, a conventional traffic camera or other video camera may be used for capturing live video. Additionally or alternatively Internet protocol (IP) cameras may also be employed, which perform embedded video compression prior to transmission. Alternatively, compressed video may be available from a video database (not shown) where video sequences from surveillance cameras are stored. 
     The motion vector calculation module to  210 , which may be part of a video compression module or the like, is executed by the processor to calculate motion vectors  212  from the incoming, live uncompressed video stream, where the vectors are the type used for video compression. The memory additionally stores one or more global histograms to  214  and one or more local histograms to  216  as well as a block comparator to  218  is executed by the processor to threshold blocks or groups of blocks within a frame (which has been segmented into a plurality of pixel blocks) to determine whether or not a block or block group is a candidate for efficient motion compensation, as described with regard to the preceding figures. That is, for every block (or block group) in the target frame, a determination is made whether the block or block group is a candidate for efficient motion compensation from the labels assigned by the learning stage ( FIG. 1 ). When determining candidacy for efficient motion compensation, the comparator and/or the processor can employ one or more of historical data, manually input data, and a priori information. If a given block or block group is not a candidate, then a traditional block matching algorithm can be performed and, optionally, histogram monitoring can be continued to detect a presence of predominant motion characteristics. If the block or block group is a candidate for efficient motion compensation as determined by the comparator  218 , then a local 2D histogram  216  of motion vector components of the target block (or block group) processed and predominant motion vector directions are determined. 
     Motion vector direction data  220  is generated from the motion vectors  212  and stored in the memory. The processor executes a search neighborhood modifier module  222  that modifies or optimizes neighborhood size, shape and orientation to perform block matching along predominant directions. That is, the processor executes a block-matching search on a modified search neighborhood. Compressed video data potentially with motion vector direction metadata  224  is then stored in the memory  206 . 
     In another embodiment, one or more of motion vector plots, histograms, video data, or the like is displayed graphically on a graphical user interface  224  that may be integral to the system, remote but operably coupled thereto, or may reside on a computer such as the computer  30  of  FIG. 1 . 
     The exemplary embodiments have been described with reference to the preferred embodiments. Obviously, modifications and alterations will occur to others upon reading and understanding the preceding detailed description. It is intended that the exemplary embodiments be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.