Patent Publication Number: US-8989442-B2

Title: Robust feature fusion for multi-view object tracking

Description:
FIELD OF THE INVENTION 
     The present invention relates generally to a system for tracking an object using a sensor network. Specifically, tracking an object with robust multi-task multi-view joint sparse representation and dynamic view weighting. 
     BACKGROUND OF THE INVENTION 
     Object tracking systems are often used in automotive and industrial settings to identify and follow objects of interest. These systems generally use sensors such as laser rangefinders, radars, or camera systems to identify and track objects. While basic tracking systems can be effective in controlled environments, they are often ineffective in real-world situations such as driving an automobile down a city street where complex and sophisticated object tracking is required. 
     Visual video tracking allows objects to be tracked using a wide range of modalities including color, shape, or brightness. However, video tracking can be a time-consuming process due to the amount of data that is contained in video. Furthermore, object recognition techniques necessary for tracking are complex and require significant computer processing. To counteract these issues, many video tracking systems only track a single modality such as color while other tracking systems use databases of stored objects. While these systems can accurately track objects under certain conditions, there exists a need for a computationally efficient video tracking system that does not track based on a single modality or rely on a database of stored objects to track a target object. 
     Modern video tracking systems also make use of algorithms to analyze sequential video frames and track the movement of targets between the frames. Different algorithms have unique strengths and weaknesses, and the choice of algorithm is largely based upon the intended use of the tracking system. Common tracking algorithms are either geared towards target representation and localization or filtering and data association. 
     Techniques utilizing target representation and localization are generally bottom-up processes with generally low computational complexity. However, these algorithms are primarily used when the camera is static or the tracking is relatively simple. Filtering and data association algorithms are generally top-down processes that incorporate additional factors into the object tracking algorithm. These algorithms are generally more computationally complex and can factor in information about background characteristics, object dynamics, and other features. These methods also are able to handle complex object interaction such as tracking moving objects behind obstructions. The video tracker may also be mounted on a moving foundation while tracking another moving object. However, these filtering and data association methods are extremely complex and require significant computational power. 
     It would therefore be beneficial for an object tracking system to combine observations from multiple views including various types of visual features to accurately track an object in a wide variety of situations. It would also be beneficial to reduce the number of individual tasks normally associated with complex visual tracking methods and jointly consider the underlying relationships between tasks across different views and different particles to tackle the problem in a unified robust multi-task formulation. 
     SUMMARY OF THE INVENTION 
     Sparse representation has recently been introduced for tracking objects by Mei and Ling in “Robust Visual Tracking and Vehicle Classification via Sparse Representation.” In the Mei reference, a tracking candidate is sparsely represented as a linear combination of target templates and trivial templates. In particle filter based tracking methods, particles are randomly sampled around the current state of the target according to a zero-mean Gaussian distribution. Each particle shares a great deal of dependencies with other particles. Multi-task learning aims to improve the performance of multiple related tasks by exploiting the intrinsic relationships among them. In “Robust Visual Tracking via Multi-Task Sparse Learning” by Zhang et al., learning the representation of each particle is viewed as an individual task and joint sparsity learning for all particles are employed. However, the Zhang reference assumes that all tasks share a common set of features, which is too restrictive and will not hold up in visual tracking applications, since outlier tasks often exist. For example, few distant particles are sampled far away from a main cluster of particles. These distant particles have little overlap with the cluster and will be considered outliers. In addition, both the Mei and Zhang references only use intensity features to model the appearance change of the target. The intensity appearance model with l 1  minimization is very robust to partial occlusion, noise, and other tracking challenges. However, it is very sensitive to shape deformation of targets such as non-rigid objects. 
     To overcome the above problems, the present tracker employs other visual cues such as color, edge, and texture as complementary features to intensity in the target appearance representation, and combines a multi-view representation with a robust multi-task learning to solve visual tracking problems. Within the tracker, the sparse representation for each view is learned as a linear combination of atoms from an adaptive feature dictionary, i.e. each view owns a sparse representation instead of sharing an identical one, which enables the tracker to capture the different statistics carried by different views. To exploit the interdependencies shared between different views and particles, the l 1,2 -norm group-sparsity regularization is imposed on the representation matrix to learn the multi-view sparse representation jointly in a multi-task manner. To handle the outlier particles from particle sampling, the sparse representation is decomposed into two collaborative parts, thereby learning representative coefficients and detecting the outlier tasks simultaneously. 
     In particular, the overall contribution of the tracker of the present invention is six-fold. First, the tracker utilizes multiple types of features in sparse representation based framework for tracking. Compared to previous trackers based on similar framework found in the Mei and Zhang references, the tracker not only is able to take advantage of the robustness to the occlusion from sparse representation, but also introduces complementary multiple view representation for robust appearance modeling. Second, the tracker treats every view in each particle as an individual task and jointly considers the underlying relationships shared among different views and different particles in a multi-task learning framework. Third, to capture the outlier tasks that frequently emerge in a particle sampling process, the tracker employs the robust multi-task scheme by decomposing the coefficient matrix into two collaborative components. Fourth, dynamic view weighting helps select the most representative views for the minimum error reconstruction by employing the entropy of each view&#39;s probability. The most discriminative views are given higher weight in the object appearance representation. Therefore, the tracker is robust to the confusions caused by some views by selecting discriminative views to model the appearance variations of the object against the background. Fifth, the Multi-Task Tracker and L1 tracker are special cases of the Multi-Task Multi-View Tracking (MTMVT) formulation of the present invention. Sixth, outlier rejection is used to identify outlier tasks and improve resampling efficiency by setting posterior probabilities of outliers to zero, thereby removing the outliers from the resampling process. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A better understanding of the present invention will be had upon reference to the following detailed description when read in conjunction with the accompanying drawings, wherein like reference characters refer to like parts throughout the several views, and in which: 
         FIG. 1A  is the first part of an illustrative example of the steps of the MTMVT; 
         FIG. 1B  is a continuation of the illustrative example from  FIG. 1A ; 
         FIG. 2  is a flow chart illustrating the steps of the MTMVT; 
         FIG. 3  depicts extracting features from a particle; 
         FIG. 4  depicts a frame having multiple tracked objects; 
         FIG. 5  is a block diagram depicting exemplary hardware utilized by the MTMVT; 
         FIG. 6  is a visualization of the structure of the matrices P k  and Q k ; 
         FIG. 7  is an example of detected outlier tasks over a sequence of frames; 
         FIG. 8  is a visualization of learned coefficients; and 
         FIG. 9  shows tracking results of the MTMVT versus other prior art trackers over an example sequence. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Sparse representation based L1 trackers of the Mei reference perform tracking by finding a sparse representation in the template subspace. The representation is then used with the particle filter framework for visual tracking. However, the intensity only based appearance representation is prone to fail for difficult scenarios such as tracking a non-rigid object such as, for example, people whose silhouette can change due to their walking, seating, etc. Employing multiple types of features has proven beneficial for tracking because the ensemble of the multiple views provides a comprehensive representation of the target appearance undergoing various changes such as illumination, deformation, etc. However, combining multiple views by simply concatenating features into a high-dimensional feature vector has proven inappropriate since different features have different statistic properties. The present tracker employs other visual cues such as color, edge, and texture as complementary features to intensity in the target appearance representation, and combines a multi-view representation with a robust multi-task learning to solve visual tracking problems. The MTMVT will first be generally described with reference to an exemplary embodiment followed by a thorough discussion of the MTMVT algorithms. 
     First, in reference to  FIG. 1 , the MTMVT  10  of the present invention tracks an object  34  using a computer processing unit (CPU)  12 , a memory unit  14 , and data frames  16  obtained from a sensor network  26 . The sensor network  26  captures a plurality of data frames  16  which are stored sequentially in the memory unit  14 . A digital video camera  18  was used in the sensor network  26 , however, the MTMVT  10  is not limited to using a digital video camera  18  and additional sensors including radar  20 , lidar  22 , or infrared  24  may be used. The CPU  12  processes the data frames  16  and implements the MTMVT  10  algorithms to track the object  34  throughout the data frames  16 . 
     Using a starting reference frame  30 , the MTMVT  10  obtains a plurality of particles  32  proximate to the location of the object  34  in the reference frame  30 . A set of multiple features  36  are extracted from each of the particles  32  and organized into feature matrices  38 . The feature matrices  38  are combined and then sparsely represented in a representation matrix  40  using a multi-task formulation. The representation matrix  40  is then decomposed into a pair of collaborative weight matrices  50 ,  52  and the reconstruction error is minimized using penalty terms. The probabilities for each feature  54  are then combined into a particle probability  56 . The tracking target result  60  is then computed by selecting the particle  32  with the highest probability  56 . 
     An overview of the MTMVT  10  method is generally shown in  FIGS. 1-2 . In step S 01  of the MTMVT  10  method, the sensor network  26  captures the data frames  16  containing the object  34  to be tracked. In the exemplary embodiment, a digital video camera  18  is used to capture the frames  16 . The frames  16  are then stored sequentially in the memory unit  14  to be processed by the CPU  12 . The CPU  12  then selects a reference frame  30  and identifies the object  34  within the reference frame  30 . In step S 02  the MTMVT  10  obtains a plurality of tracking target templates or particles  32  from the reference frame  30 . The particles  32  are located in an area of the reference frame  30  proximate to the location of the object  34 . A plurality of rectangular particles  32  are shown encompassing various amounts of the object  34  in the reference frame  30 , although the number and location of the particles  32  is only demonstrative. The particles  32  are shown in greater detail in  FIG. 3 . Specifically, the reference frame  30  has a plurality of particles  32  surrounding the object  34  to be tracked. 
     In step S 03  the CPU  12  extracts a set of features  36  from the particles  32  gathered from the reference frame  30 . These features  36  can include particle color, shape, texture, or other features such as heat signature depending on the sensors in the senor network  26 . The transition from step S 02  to step S 03  is shown in  FIG. 3 , where the CPU  12  extracts a demonstrative set of features  36  from a particle. The features  36  for each particle  32  are indexed in a plurality of feature matrices  38  and stored in the memory unit  14 . The process then proceeds to step S 04  and S 05  where additional particles  32  are sampled from data frames  16  subsequent to the reference frame  30  and then the same set of features  36  are extracted from these new particles  32 . The set of features  36  corresponding to the particles  32  sampled from subsequent frames are again indexed in the feature matrices  38  and stored in the memory unit  14 . 
     Once the set of feature matrices  38  are populated with particle data, the MTMVT  10  sparsely represents the set of features  36  into a representation matrix  40  using a multi-task formulation. Other tracking methods treat each particle  32  or each feature/view  36  as a single task, which increases the computational demands of the tracker. However, the MTMVT  10  uses a multi-task formulation to combine each particle  32  and feature  36  into a single task. This improves the computational efficiency of the tracker  10  and will be explained in greater detail below. 
     Following the sparse representation of the particles  32 , in step S 06  the representation matrix  40  is decomposed into a pair of collaborative weight matrices  50 ,  52 . The collaborative weight matrices  50 ,  52  are then minimized to identify and remove outliers  62 . By decomposing the representation matrix  40  and minimizing the resulting collaborative weight matrices  50 ,  52 , the MTMVT&#39;s  10  robustness to outliers  62  is improved. This in turn leads to a more accurate reconstruction and reduced error in the MTMVT  10 . 
     Having minimized the collaborative weight matrices  50 ,  52 , the MTMVT  10  calculates the feature probabilities  54  in step S 07 . As shown in  FIG. 1(A) , the dynamic view weighting of each particle  32  encompasses a feature probability  54  previously extracted in step S 03 . By separating the feature probabilities  54  by feature  36 , the MTMVT  10  can evaluate the effectiveness of each feature  36  and appropriately weight the result. The individual feature probabilities  54  are then combined to form the particle probability  56 . The entropy of the normalized particle probability  56  is calculated in step S 08  and applied to the particle probability  56 . 
     Once the MTMVT  10  has the particle probability  56  for all the particles  32 , the tracking target result  60  is identified as the particle  32  with the maximum particle probability  56 . As shown in  FIG. 1(B) , the tracking target result  60  is the particle  32  selected to represent the object  34 . Of all the particles  32  identified in step S 02 , the tracking target result  60  has the highest particle probability  56  of representing the object  34 . After the tracking target result  60  has been identified in step S 09 , the MTMVT  10  returns back to step S 01  and continues tracking objects. 
     As shown in  FIG. 4 , while the MTMVT  10  was described for tracking a single object  34 , in some instances the MTMVT  10  is used to track additional objects  80 . In this situation, additional particles  82  are obtained for the additional objects  80  and the MTMVT  10  is performed to track the additional objects  80 . 
     The specific algorithms used by the MTMVT  10  will now be described and explained in greater detail. The state variable y t  describes the location and shape of a target or an object  34  at time frame t. The tracking problem can be formulated as an estimation of the state probability p(y t |x 1:t ), where x 1:t ={x 1 , . . . , x t } represents the observations from previous t frames. To model the observation likelihood p(x t |y t ), a region corresponding to state y t  is first cropped from the current frame. The region is then normalized and reshaped to a 1D vector x, which is used as a target candidate. 
     Sparse Representation Based Tracker. In the Mei reference, the sparse representation of x is formulated as a minimum error reconstruction through a regularized l 1  minimization function with nonnegativity constraints 
                         min   w     ⁢           ⁢            Mw   -   x          2   2       +     λ   ⁢          w        1         ,       s   .   t   .           ⁢   w     ≥   0     ,           (   1   )               
where M=[D, I, −I] is an over-complete dictionary that is composed of target template set D and positive and negative trivial template sets I and −I. Each column in D is a target template generated by reshaping pixels of a candidate region into a column vector; and each column in the trivial template sets is a unit vector that has only one nonzero element. w=[α T , e +T , e −T ] T  is composed of target coefficients α and positive and negative trivial coefficients e + , e −  respectively.
 
     Finally, the observation likelihood is derived from the reconstruction error of x as 
                       p   ⁡     (     x   ❘   y     )       =       1   Γ     ⁢   exp   ⁢           ⁢     {       -   α     ⁢            Da   -   x          2       }         ,           (   2   )               
where a is obtained by solving the l 1  minimization (1), α is a constant controlling the shape of the Gaussian kernel, and Γ is a normalization factor.
 
     While intensity appearance modeling with l 1  minimization is very robust to occlusion, noise, and other tracking changes, it is very sensitive to shape deformation of the target such as with non-rigid objects like humans. The MTMVT overcomes these shortcomings by considering other visual features like shape and texture as complementary to the intensity vector in the target appearance representation. The MTMVT combines multi-view representation with robust multi-task learning and dynamic view weighting to solve complex visual tracking problems. 
     Robust Multi-Task Multi-View Sparse Learning. The MTMVT considers n particle samples where each sample has K different modalities of features (e.g., color, shape and texture). For each modality index k=1, . . . , K, denote X k  ∈ R d     k     ×n  as the feature matrix which is a stack of n columns of particle image feature vectors of dimension d k , where d k  is the dimension for the kth modality. The target dictionary is denoted as D t   k  ∈ R d     k     ×N  where each column is a target template from modality k, and where N is the number of target templates. It can be combined with a background dictionary D b   k  or trivial templates I d     k    to construct the evaluated dictionary M k =[D t   k , D b   k ] or M k =[D t   k , I d     k   ]. Without loss of generality, I d     k    is considered as a special case of background dictionary and D b   k  is used to denote both D b   k  and I d     k    in the rest of the section. 
     Based on the fact that the majority of the particles are relevant and outliers often exist, a robust multi-task learning scheme is induced to capture the correlations of each task. The MTMVT jointly evaluates K modality feature matrices {X 1 , . . . , X K } with n samples and learns the latent representations {W 1 , . . . , W K }. The decomposed matrices W k s enable different views of particles that have different learned representations and therefore exploit the independencies of each view and capture the different statistic properties. Moreover, each representation matrix W k  is constructed by two collaborative components P k  and Q k , where P k  is regularized by row sparse constraint, which assumes all particles sharing the same basis, while Q k  is regularized by column sparse constraint, which enables the capture of the outlier tasks. 
     The MTMVT considers two different scenarios based on the number of basis vectors in the dictionary for different modalities. First, when the M k s have the same number of columns, i.e. the same number of basis vectors in the dictionary, the weight matrix W k  for each modality is decomposed into two components P k  and Q k . Since each modality feature matrix has the same column, the corresponding decomposed weight matrix P k  and Q k  can be stacked horizontally to form a bigger matrix P and Q that consist of the weights across all the modalities. Group lasso penalty is applied on row groups of the first component P for capturing the shared features among all tasks over all modalities. The same group lasso penalty is imposed on column groups of the second component Q to identify the outlier tasks simultaneously. 
     
       
         
           
             
               
                 
                   
                     
                       
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 W   k   =P   k   +Q   k   , P=[P   1   , . . . , P   K   ], Q=[Q   1   , . . . , Q   K ]  (4)
 
Secondly, when the M k s have a different number of columns, the corresponding decomposed weight matrix P k  and Q k  can not be stacked to a bigger matrix as the first situation. The MTMVT uses two solutions to handle this situation.
 
     Situation one. If P k  is decomposed into the target weight matrix P k  and background weight matrix P b   k , the same technique that was used on P t   k  can be applied to P b   k  since they have the same number of columns due to the shared target templates. The same decomposition can be applied to Q k  as well, and the minimization function is as follows. 
     
       
         
           
             
               
                 
                   
                     
                       
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 W   k   =P   k   +Q   k   , P   k   =[P   t   k   , P   b   k   ], Q   k   =[Q   t   k   , Q   b   k   ], P   t   =[P   t   1   , . . . , P   t   K   ], Q   t   =[Q   t   1   , . . . , Q   t   K ]  (6)
 
     Situation two. If the matrices M k s are padded with zero columns to make them the same number of columns, the same technique as the first situation can be used. The coefficients associated with the zero columns will be zeros based on the sparsity constraints from l i  regularization and do not have impact on the minimization function in terms of the solution. Without loss of generality, it is assumed that the M k s are sorted in descending order of the number of columns n k , that is, n 1 ≧n 2 ≧ . . . ≧n K . The new {circumflex over (M)} k  is defined as the zero padded matrix of M k , that is, {circumflex over (M)} k =[M k , 0 k ], where 0 k  ∈ R d     k     ×(n     1     -n     k     )  and every element in 0 k  is zero. The M k  in Equation (3) is replaced with {circumflex over (M)} k  and the same minimization function is solved. 
       FIG. 6  illustrates the structure of the learned coefficient matrices P  50  and Q  52 . The entries with different shading represents different learned values. The white entries in P  50  and Q  52  indicate the zero rows and columns In the exemplary case, there are four particles  32 , where the second particle  32  is an outlier  62  whose coefficients in Q  52  comprise large values. 
       FIG. 8  is a visualization depicting an empirical example of the learned sparse coefficients. A plurality of particles  32  are extracted from the reference frame  30  about the object  34 . The collaborative weight matrices P k    50  and Q k    52  are visualized to show the learned values for all the particles  32 . The four columns of P k    50  and Q k    52  represent different features and the rows are the representations of the coefficients of the particles  32 . The spots in the second column of Q k    52  indicate the presence of outliers  62 . The two bottom boxes illustrate the coefficients of the tracking target result  60  and an outlier  62 . 
     Dynamic View Weighting. In the reference of the tracking result, each view is weighted based on the discriminativity of the tracking. View discriminativity is defined as the entropy of the particle probabilities. The probability of the particle i for each view k is defined as 
                       p   i   k     =       1   Γ     ⁢   exp   ⁢           ⁢     {       -   α     ⁢                M   k     ⁢     W   i   k       -     X   i   k            2       }         ,           (   7   )               
where W i   k  is the ith column of the kth view which corresponds to the ith particle, the same definition for X i   k . The entropy of the normalized particle probabilities for kth view is defined
 
                       h   k     =     -       ∑     i   =   1     n     ⁢           ⁢       p   i   k     ⁢   log   ⁢           ⁢     (     p   i   k     )             ,         ∑     i   =   1     n     ⁢           ⁢     p   i   k       =   1             (   8   )               
The probability of the tracking candidate i is defined as
 
                       p   i     =       1   Γ     ⁢   exp   ⁢           ⁢     {       -     α   k       ⁢       ∑     k   =   1     K     ⁢           ⁢       h   k     ⁢                M   k     ⁢     W   i   k       -     X   i   k            2           }         ,           (   9   )               
The tracking result is the particle that has the maximum probability p max =max{p 1 , . . . , p n }.
 
     A more general formulation of equation (3) can be written as 
                         min     W   ,   P   ,   Q       ⁢           ⁢       ∑     k   =   1     K     ⁢           ⁢                M   k     ⁢     W   k       -     X   k            F   2         +       λ   1     ⁢          P          1   ,   2         +       λ   2     ⁢            Q   T            1   ,   2           ,           (   10   )               
where ∥P∥ p-q =Σ i (((Σ j p i,j   q ) 1/q ) p ) 1/p  is the l p,q  norm of P. To restrict a sparse number of dictionary templates to be selected by all particles across all views, let p=1, then ∥P∥ 1,q =Σ i (Σ j p i,j   q ) 1/q  is derived, which encourages P to be row sparse. For the options of q, three widely studied mixed norms, q ∈ {1, 2, ∞} are selected. Next, equation (10) is discussed where different combinations of λ 2 , q, K yield different trackers. If the tracker is restricted to the case of λ 2 =+∞ and K=1 for a single view multi-task problem, then Q=0. So (10) is degenerated to the formula as follows
 
                         min   P     ⁢           ⁢       1   2     ⁢            MP   -   X          F   2         +       λ   1     ⁢          P          p   ,   q           ,           (   11   )               
which is used in Multi-Task Tracking (MTT). Furthermore, if q=1, the obtained formulation is intrinsically the same as (1) which is the original l 1  tracker. In this way, both the MTT tracker and the l 1  tracker can be regarded as special cases of the MTMVT algorithm in the single view scenario.
 
     Another single view version of the MTMVT algorithm can be discussed where K=1 and appropriately setting λ 2 &gt;0 in which some nonzero columns of Q will be obtained if outliers exist. Specifically, if q=2, a robust MTT tracker is derived as 
                         min   P     ⁢           ⁢       1   2     ⁢            MW   -   X          F   2         +       λ   1     ⁢          P          1   ,   2         +       λ   2     ⁢            Q   T            2   ,   1           ,           (   12   )               
where W=P+Q, and the component P can exploit the underlying relationships of majority particles, while the component Q is able to capture the outlier tasks simultaneously, which yields more robust representations.
 
     Outlier Rejection. Although a majority of particles would share the same dictionary basics, some outlier tasks may exist. These are the particles sampled far away from the target that have little overlap with other particles. The MTMVT in (3) is capable of capturing the outlier tasks by introducing the auxiliary coefficient matrix Q. In particular, if the sum of the l 1  norm of the coefficients for the corresponding ith particle is bigger than an adaptive threshold γ, as 
                         ∑     k   =   1     K     ⁢           ⁢          Q   i   k            &gt;   γ     ,           (   13   )               
where Q i   k  is the ith column of Q k , then it will be identified as a outlier and its observation likelihood will be set to zero, and thus the outliers will be ignored in the particle resampling process. Therefore, samples are utilized more efficiently without wasting samples on the outliers. By denoting the number of detected outlier tasks as n o , the threshold γ is updated as follows
 
                   {                 γ   new     =       γ   old     ⁢   κ       ,       n   o     &gt;     N   o                       γ   new     =       γ   old     ⁢     /     ⁢   κ       ,       n   o     =   0                     γ   new     =     γ   old       ,     0   &lt;     n   o     ≤     N   o               ,             (   14   )               
where κ is a scaling factor, and N o  is the maximum number of the outlier detected.  FIG. 7  illustrates some examples showing outliers  62  that are detected and rejected.
 
     Experiments. To evaluate the effectiveness of the present invention, the MTMVT was implemented using four complementary features as four different views. The tracker was extensively tested in several publicly available challenging sequences, and compared it with five other popular trackers or related ones including L1 Tracker (L1T), Multi-Task Tracking (MTT), Visual Tracking Decomposition (VTD), tracking with Multiple Instance Learning (MIL), and Incremental Learning for Visual Tracking (IVT). The experiments were conducted by running source codes provided by the original authors. All the parameters are set to default. 
     Implementation Details. To take advantage of complementary features, four popular features including intensity, color histograms, Histograms of Oriented Gradients (HOG) and Local binary patterns (LBP) were employed. Histograms of Oriented Gradients (HOG) is a gradient-based feature that captures edge distribution of an object. Local binary patterns (LBP) is a powerful feature for representing the object texture. Some work combining LBP and HOG has demonstrated superior performance in pedestrian detection tasks. Moreover, to ensure the quality of extracted features, a simple but effective illumination normalization method was applied before the feature extraction. 
     For all reported experiments the following settings were used: λ 1 =λ 2 =0.5, the number of particles n=400 (same as UT and MTT), the number of template samples N=10, and the template updating threshold θ=30. The template of intensity was set to one third size of the initial target (half size for those whose shorter side is less than 20), while the color histograms, HOG, LBP were extracted in a bigger template that was double the size of the intensity template. All sequences were resized to 320×240 for easy comparison and evaluation. 
     Qualitative Comparison. The Animal and Car4 sequences shown in  FIG. 9  demonstrate tracking the head of fast running deer and a moving car, respectively. The main challenges of these two sequences include fast motion, background clutter, scale changes and illumination changes, etc. For the Animal sequence, only MIL and MTMVT succeed to track the target over the whole sequence, while MTT only tracks most of the frames. The IVT gradually drifts from the target starting in the second frame and totally loses the target by the seventh frame. The L1T fails during the fast motion and motion blur. Apparently, the multi-task manner enables MTT and MTMVT to be more robust than L1T. However, MTT is not as robust as MTMVT since MTMVT takes advantage of the complementary features and is capable of detecting outlier tasks. In the Car4, both MTMVT and IVT perfectly track the moving car despite the dramatic illumination changes and scale changes, which have been shown in the second row of  FIG. 9 . By contrast, VTD and MIL lose the target and UT tends to be unstable when the car is moving under the bridge, which leads to significant illumination change. 
     Quantitative Comparison. For more intuitive comparison, the average position errors for ten test sequences taken from a public domain are summarized in Table 1. It shows that the MTMVT achieved the best average performance over all tested sequences and beats the other five selected trackers. Only MTMVT successfully tracked all targets in the experiments, indicating that the tracker of the present invention is not as sensitive to shape deformation as previous trackers due to the effective use of complementary features. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Average position error (pixels).  
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Seq. name 
                 L1T 
                 MTT 
                 VTD 
                 MIL 
                 IVT 
                 MTMVT 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Animal 
                 23.1 
                 7.3 
                 104.0 
                   3.7   
                 143.9 
                 
                   6.9 
                 
               
               
                 Car4 
                 5.7 
                 2.0 
                  28.8 
                 59.4 
                   1.4   
                 
                   1.6 
                 
               
               
                 David 
                 26.1 
                 38.7 
                  53.8 
                 
                   16.0 
                 
                  20.1 
                 
                   3.5 
                 
               
               
                 Kitesurf 
                 34.6 
                 43.6 
                 102.3 
                   3.3   
                  64.5 
                 
                   4.2 
                 
               
               
                 Shaking 
                 59.9 
                 11.9 
                  8.4 
                   8.2   
                 112.2 
                 
                   4.5 
                 
               
               
                 Faceocc2 
                 9.1 
                 6.9 
                  23.0 
                 13.9 
                   4.7   
                 
                   5.6 
                 
               
               
                 Sylv 
                 16.2 
                 
                   7.1 
                 
                  23.9 
                 17.7 
                  27.9 
                 
                   3.2 
                 
               
               
                 Tiger1 
                 
                   20.7 
                 
                 30.9 
                  55.5 
                 26.3 
                 122.3 
                 
                   8.1 
                 
               
               
                 Bolt 
                 197.4 
                 74.8 
                   9.3   
                 14.2 
                 158.8 
                 
                   6.0 
                 
               
               
                 DH 
                 18.5 
                 4.3 
                   3.7   
                  4.9 
                  62.0 
                 
                   4.1 
                 
               
               
                 Skating1 
                 33.9 
                 
                   6.6 
                 
                  48.0 
                 41.4 
                  53.9 
                 
                   4.7 
                 
               
               
                 Gym 
                 93.8 
                 71.8 
                   5.9   
                 25.4 
                  32.8 
                 
                   7.3 
                 
               
               
                 Average 
                 41.5 
                 24.5 
                  26.9 
                 
                   23.1 
                 
                  48.7 
                 
                   4.7 
                 
               
               
                   
               
               
                   Underlined  numbers indicate the best performance, while  italics  indicate the second. 
               
               
                 Note that the average is computed as weighted average with respect to the sequence length. 
               
            
           
         
       
     
     As shown, the tracker of the present invention is a robust multi-task multi-view joint sparse learning method for particle filter based tracking. By appropriately introducing the l 1,2  norm regularization, the method not only exploits the underlying relationships shared by different views and different particles, but also captures the frequently emerging outlier tasks ignored by other trackers. The MTMVT was elaborately implemented using four types of complementary features, i.e. intensity, color histogram, HOG and LBP, and extensively tested on several challenging sequences. The experimental results proved that the MTMVT is able to take advantage of multi-view clues and correctly identify the outlier tasks. Compared with other five popular trackers, the MTMVT demonstrates a generally superior performance Furthermore, the proposed method can be readily extended to handle cues obtained from different types of sensors, rather than conventional video cameras. Having described the invention, however, many modifications thereto will become apparent to those skilled in the art to which it pertains without deviation from the spirit of the invention as defined by the scope of the appended claims.