Multi-part sparse representation in random crowded scenes tracking

Shao, Jie; Dong, Nan; Tong, Minglei

doi:10.1016/j.patrec.2012.07.008

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…To overcome the tracking issues mentioned above, a variety of SR-based algorithms have been proposed in the literature [14][15][16][17][18]. Mei et al [22] used an SR algorithm for the first time for object tracking.…”

Section: Related Workmentioning

confidence: 99%

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Parallel algorithm implementation for multi‐object tracking and surveillance

Elbahri

Taleb

Kpalma³

et al. 2016

IET Computer Vision

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International audienceA recently developed sparse representation algorithm, has been proved to be useful for multi-object tracking and this study is a proposal for developing its parallelisation. An online dictionary learning is used for object recognition. After detection, each moving object is represented by a descriptor containing its appearance features and its position feature. Any detected object is classified and indexed according to the sparse solution obtained by an orthogonal matching pursuit (OMP) algorithm. For a real-time tracking, the visual information needs to be processed very fast without reducing the results accuracy. However, both the large size of the descriptor and the growth of the dictionary after each detection, slow down the system process. In this work, a novel accelerating OMP algorithm implementation on a graphics processing unit is proposed. Experimental results demonstrate the efficiency of the parallel implementation of the used algorithm by significantly reducing the computation time

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Section: Related Workmentioning

confidence: 99%

“…In the literature, there are many SR-based object tracking methods [14][15][16][17][18]. However, their computational cost increases a lot throughout the process, while the effective object tracking systems require a real-time process.…”

Section: Introductionmentioning

confidence: 99%

Parallel algorithm implementation for multi‐object tracking and surveillance

Elbahri

Taleb

Kpalma³

et al. 2016

IET Computer Vision

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“…Most recent research works have focused on sparse representation to deal with the problem of object tracking [25,26,27,28,29]. Lu et al [15] present some experimentation of OMP and LARS [30] for tracking.…”

Section: Related Workmentioning

confidence: 99%

A Novel Object Position Coding for Multi-Object Tracking using Sparse Representation

Elbahri¹,

Kpalma²,

Taleb³

et al. 2015

IJIGSP

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Abstract-Multi-object tracking is a challenging task, especially when the persistence of the identity of objects is required. In this paper, we propose an approach based on the detection and the recognition. To detect the moving objects, a background subtraction is employed. To solve the recognition problem, a classification system based on sparse representation is used. With an online dictionary learning, each detected object is classified according to the obtained sparse solution. Each column of the used dictionary contains a descriptor representing an object. Our main contribution is the representation of the moving object with a descriptor derived from a novel representation of its 2-D position and a histogram-based feature, improved by using the silhouette of this object. Experimental results show that the approach proposed for describing moving objects, combined with the classification system based on sparse representation provides a robust multi-object tracker in videos involving occlusions and illumination changes.

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“…Besides the IVT, Bao et al [3] described the object appearance using a sparse representation [4]- [6] over a template set and propose a new minimization model and a much faster numerical solver for the l 1 tracker [7], [8]; Kwon and Lee [9], [10] constructed appropriate sampled trackers using now accepted samples and integrate the constructed trackers to track the object. Other generative methods were proposed in [11]- [14].…”

mentioning

confidence: 99%

Online State-Based Structured SVM Combined With Incremental PCA for Robust Visual Tracking

Yin

Wang

et al. 2015

IEEE Trans. Cybern.

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In this paper, we propose a robust state-based structured support vector machine (SVM) tracking algorithm combined with incremental principal component analysis (PCA). Different from the current structured SVM for tracking, our method directly learns and predicts the object's states and not the 2-D translation transformation during tracking. We define the object's virtual state to combine the state-based structured SVM and incremental PCA. The virtual state is considered as the most confident state of the object in every frame. The incremental PCA is used to update the virtual feature vector corresponding to the virtual state and the principal subspace of the object's feature vectors. In order to improve the accuracy of the prediction, all the feature vectors are projected onto the principal subspace in the learning and prediction process of the state-based structured SVM. Experimental results on several challenging video sequences validate the effectiveness and robustness of our approach.

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Multi-part sparse representation in random crowded scenes tracking

Cited by 11 publications

References 21 publications

Parallel algorithm implementation for multi‐object tracking and surveillance

Parallel algorithm implementation for multi‐object tracking and surveillance

A Novel Object Position Coding for Multi-Object Tracking using Sparse Representation

Online State-Based Structured SVM Combined With Incremental PCA for Robust Visual Tracking

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