Local Sparse Appearance Model Research Articles

Robust particle tracking via spatio-temporal context learning and multi-task joint local sparse representation

Particle filters have been proven very successful for non-linear and non-Gaussian estimation problems and extensively used in object tracking. However, high computational costs and particle decadency problem limit its practical application. In this paper, we present a robust particle tracking approach based on spatio-temporal context learning and multi-task joint local sparse representation. The proposed tracker samples particles according to the confidence map constructed by the spatio-temporal context information of the target. This sampling strategy can ameliorate problems of sample impoverishment and particle degeneracy, target state distribution to obtain robust tracking performance. In order to locate the target more accurately and be less sensitive to occlusion, the local sparse appearance model is adopted to capture the local and structural information of the target. Finally, the multi-task learning where the representations of particles are learned jointly is employed to further improve tracking performance and reduce overall computational complexity. Both qualitative and quantitative evaluations on challenging benchmark image sequences have demonstrated that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Dual‐scale weighted structural local sparse appearance model for object tracking

It is a great challenge to develop an effective appearance model for robust visual tracking due to various interfering factors, such as pose change, occlusion, background clutter etc. More and more visual tracking methods tend to exploit the local appearance model to deal with the above challenges. In this study, the authors present a simple yet effective weighted structural local sparse appearance model, which can better describe the target appearance information through patch‐based generative weight. To further improve the robustness of tracking, they implement this appearance model on two‐scale patches. The two derived appearance models are then combined to form a collaborative model to play their advantages. Extensive experiments on the tracking benchmark dataset show that the proposed method performs favourably against several state‐of‐the‐art methods.

Visual tracking via context-aware local sparse appearance model

Most existing local sparse trackers are prone to drifting away as they do not make use of discriminative information of local patches. In this paper, we propose an effective context-aware local sparse appearance model to alleviate the drift problem caused by background clutter and occlusions. First, considering that different local patches should have different impacts on the likelihood computation, we present a novel Impact Allocation Strategy (IAS) with integration of the spatial-temporal context. Varying positive impact factors are adaptively assigned to different local patches based on their ability distinguishing the spatial context, which provides discriminative information to prevent the tracker from drifting. Furthermore, we exploit temporal context to introduce some historical information for more accurate locating. Second, we present a new patch-based dictionary update method being able to update each patch independently with the validation of effectiveness. On the one hand, we introduce sparsity concentration index to check whether the local patch to be updated is a valid local patch from the target object. On the other hand, spatial context is further employed to eliminate the effect of the background. Experimental results show the superiority and competitiveness of the proposed method on the benchmark data set compared to other state-of-the-art algorithms.

Object tracking with an effective composite similarity

How to achieve a robust performance remains an intractable problem with the various object tracking algorithms, due to some unfavorable factors, e.g., occlusion, appearance change, etc. A robust object-tracking approach is proposed based on a composite similarity measure. Based on the first several frames in a video sequence, an initial dictionary is constructed by searching for the best candidates via the kd-tree method. For a target candidate, a likelihood value is computed by weighting the sparse representation coefficients of local patches obtained via the adaptive structural local sparse appearance (ASLSA) model. In order to alleviate the effect of occlusion, a smoothing operation is designed in computing the likelihood value. Meanwhile, after obtaining the codebook by applying the k-means clustering algorithm on the randomly sampled positive and negative templates, a confidence value is computed, via the sparse collaborative model (SCM), for each of the candidates. Furthermore, a composite similarity measure is devised, which combines the likelihood value and the confidence value for each candidate. Finally, the best candidate is searched for, according to the composite similarity measure. Due to the complementarity of SCM and ASLSA, the proposed algorithm is a more robust approach to object tracking. Experimental results on several challenging sequences demonstrate the effectiveness and feasibility of the proposed method.

Dual-scale structural local sparse appearance model for robust object tracking

Recently, sparse representation has been applied in object tracking successfully. However, the existing sparse representation captures either the holistic features of the target or the local features of the target. In this paper, we propose a dual-scale structural local sparse appearance (DSLSA) model based on overlapped patches, which can capture the quasi-holistic features and the local features of the target simultaneously. This paper first proposes two-scales structural local sparse appearance models based on overlapped patches. The larger-scale model is used to capture the structural quasi-holistic feature of the target, and the smaller-scale model is used to capture the structural local features of the target. Then, we propose a new mechanism to associate these two scale models as a new dual-scale appearance model. Both qualitative and quantitative analyses on challenging benchmark image sequences indicate that the tracker with our DSLSA model performs favorably against several state-of-the-art trackers.

Robust visual tracking based on generative and discriminative model collaboration

Effective object appearance model is one of the key issues for the success of visual tracking. Since the appearance of a target and the environment changes dynamically, the majority of existed visual tracking algorithms tend to drift away from targets. To address this issue, we propose a robust tracking algorithm by integrating the generative and discriminative model. The object appearance model is made up of generative target model and a discriminative classifier. For the generative target model, we adopt the weighted structural local sparse appearance model combining patch based gray value and Histogram of Oriented Gradients feature as the patch dictionary. By sampling positives and negatives, alignment-pooling features are obtained based on the patch dictionary through local sparse coding, then we use support vector machine to train the discriminative classifier. The proposed method is embedded into a Bayesian inference framework for visual tracking. A combined matching method is adopted to improve the proposal distribution of the particle filter. Moreover, in order to adapt the situation change, the patch dictionary and discriminative classifier are updated by incremental learning every five frames. Experimental results on some publicly available benchmarks of video sequences demonstrate the accuracy and effectiveness of our tracker.

Visual Tracking via Coarse and Fine Structural Local Sparse Appearance Models.

Sparse representation has been successfully applied to visual tracking by finding the best candidate with a minimal reconstruction error using target templates. However, most sparse representation-based tracking methods only consider holistic rather than local appearance to discriminate between target and background regions, and hence may not perform well when target objects are heavily occluded. In this paper, we develop a simple yet robust tracking algorithm based on a coarse and fine structural local sparse appearance model. The proposed method exploits both partial and structural information of a target object based on sparse coding using the dictionary composed of patches from multiple target templates. The likelihood obtained by averaging and pooling operations exploits consistent appearance of object parts, thereby helping not only locate targets accurately but also handle partial occlusion. To update templates more accurately without introducing occluding regions, we introduce an occlusion detection scheme to account for pixels belonging to the target objects. The proposed method is evaluated on a large benchmark data set with three evaluation metrics. Experimental results demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Robust residual error consistent tracker with ranking mechanism

In the paper, we propose a novel structural local sparse representation based residual error consistent ranking tracker. In our tracker, candidate targets are linearly combined by using the structural local sparse appearance model. To encourage temporal consistency, a residual error consistency term is designed to constraint the objective function of sparse representation. Based on the objective function, the similarity information is extracted from both coefficients and residual errors of sparse coding. For extracting similarity information from coefficients, the alignment-pooling algorithm is applied to obtain pooled features. For extracting similarity information from residual errors, we develop a residual error score. For different natures of residual error scores and pooled features, a ranking mechanism is proposed to fuse them. The dictionary updating scheme uses the ranking results of the predicted targets to determine which of them are collected for updating. Our tracker performs favorably against 6 state-of-the-art trackers on 18 challenging sequences.

Robust object tracking based on local region sparse appearance model

We propose a robust object tracking algorithm based on local region sparse appearance model in this paper. In this algorithm, the object is divided into several sub-regions, and the sparse dictionaries are obtained by clustering in each sub-region. Therefore spatial structure information of the object can be captured well, and the change of object appearance can be also resisted effectively. First, the object is divided into many small patches. Then the object is divided into several sub-regions according to patch distribution again. The establishment of object dictionary base is based on combination of the dictionaries from all the sub-regions, and then space alignment between different parts of the object can be achieved. Meanwhile, noise removal and other operations in the existing sparse reconstruction error maps are performed to retain valuable information. In the updating framework, a novel flexible template set update mechanism is introduced in this paper. In this update mechanism, valuable object samples will be put into the template set. If samples are not valuable, they should not be put into the template set, even when the template set is not full. Then we use patch sparse coefficient histogram of updated templates to extract time domain information of the object in the form of weighted sum. Therefore, it can provide a reliable template basis for obtaining good candidate object. In addition, when tracking result deviates from the actual position of the object, we use a dynamic sub-region resampling method based on cosine angle to correct the position deviation timely. Therefore this method can effectively prevent the object from being completely lost. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Robust Visual Tracking Using Local Sparse Appearance Model and K-Selection

Online learned tracking is widely used for its adaptive ability to handle appearance changes. However, it introduces potential drifting problems due to the accumulation of errors during the self-updating, especially for occluded scenarios. The recent literature demonstrates that appropriate combinations of trackers can help balance the stability and flexibility requirements. We have developed a robust tracking algorithm using a local sparse appearance model (SPT) and K-Selection. A static sparse dictionary and a dynamically updated online dictionary basis distribution are used to model the target appearance. A novel sparse representation-based voting map and a sparse constraint regularized mean shift are proposed to track the object robustly. Besides these contributions, we also introduce a new selection-based dictionary learning algorithm with a locally constrained sparse representation, called K-Selection. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the recent literature.

Multi-part sparse representation in random crowded scenes tracking

A multi-part sparse representation method is used in random crowded scenes for pedestrian tracking in this paper. In crowded scenes, there are random movements and orderly movements. Random movements are defined as the motion of each individual in the crowd appears to be unique, and different participants move in different directions over time. This means methods about multi-model in motion flows are not available. As a result, we propose a fully unsupervised tracking algorithm based on a multi-part local sparse appearance model. Based on the facts that only non-occluded segments of a target are effective in feature matching, while the occluded segments are the disturbed ones, our algorithm employs a multi-part sparse reconstruction code. The method is used on target segments in stead of the whole target, and implemented by solving an l1 regularized least squares problem. The segment group with the smallest projection error will be taken as the tracking result. All the segment groups are drawn based on a density distribution in a Bayesian state inference framework. After tracking process in each frame, the template dictionary will be jointly inferred and updated to adapt appearance variation. We test the method on numerous videos including different type of very crowded scenes with serious occlusion and illumination variation. The proposed approach demonstrates excellent performance in comparison with previous methods.