Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation

Appearance models play an important role in visual tracking. Effective modeling of the appearance of tracked objects is still a challenging problem because of object appearance changes caused by factors, such as partial occlusion, illumination variation and deformation, etc. In this paper, we propose a tracking method based on the patch descriptor and the structural local sparse representation. In our method, the object is firstly divided into multiple non-overlapped patches, and the patch sparse coefficients are obtained by structural local sparse representation. Secondly, each patch is further decomposed into several sub-patches. The patch descriptors are defined as the proportion of sub-patches, of which the reconstruction error is less than the given threshold. Finally, the appearance of an object is modeled by the patch descriptors and the patch sparse coefficients. Furthermore, in order to adapt to appearance changes of an object and alleviate the model drift, an outlier-aware template update scheme is introduced. Experimental results on a large benchmark dataset demonstrate the effectiveness of the proposed method.

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A Particle Swarm Optimization Algorithm with Local Sparse Representation for Visual Tracking

Journal of Computers ◽

10.4304/jcp.9.9.2230-2238 ◽

2014 ◽

Vol 9 (9) ◽

Author(s):

Xu Cheng ◽

Nijun Li ◽

Tongchi Zhou ◽

Lin Zhou ◽

Zhenyang Wu

Keyword(s):

Particle Swarm Optimization ◽

Sparse Representation ◽

Visual Tracking ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Local Sparse Representation

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Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking

Computational Intelligence and Neuroscience ◽

10.1155/2016/5894639 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13

Author(s):

Honghong Yang ◽

Shiru Qu

Keyword(s):

Sparse Representation ◽

Object Tracking ◽

Visual Information ◽

Likelihood Function ◽

Appearance Model ◽

Tracking Accuracy ◽

Neighborhood Information ◽

Object Appearance ◽

Template Library ◽

Appearance Models

Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness.

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Drift-free tracking via the construction of an effective dictionary

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420929651 ◽

2020 ◽

Vol 17 (3) ◽

pp. 172988142092965

Author(s):

Li Zhao ◽

Pengcheng Huang ◽

Fei Liu ◽

Hui Huang ◽

Huiling Chen

Keyword(s):

Sparse Representation ◽

Visual Tracking ◽

Tracking Algorithm ◽

Linear Combinations ◽

Computational Burden ◽

Tracking Algorithms ◽

Tracking Process ◽

Appearance Changes

Template dictionary construction is an important issue in sparse representation (SP)-based tracking algorithms. In this article, a drift-free visual tracking algorithm is proposed via the construction of an effective template dictionary. The constructed dictionary is composed of three categories of atoms (templates): nonpolluted atoms, variational atoms, and noise atoms. Moreover, the linear combinations of nonpolluted atoms are also added to the dictionary for the diversity of atoms. All the atoms are selectively updated to capture appearance changes and alleviate the model drifting problem. A bidirectional tracking process is used and each process is optimized by two-step SP, which greatly reduces the computational burden. Compared with other related works, the constructed dictionary and tracking algorithm are both robust and efficient.

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Robust Object Tracking Based on Structural Local Sparse Representation and Incremental Subspace Learning

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.2388 ◽

2013 ◽

Vol 765-767 ◽

pp. 2388-2392 ◽

Cited By ~ 1

Author(s):

Ying Shi ◽

Yan Zhao ◽

Nian Mao Deng

Keyword(s):

Sparse Representation ◽

Spatial Information ◽

Partial Information ◽

State Of The Art ◽

Subspace Learning ◽

Robust Tracking ◽

Tracking Method ◽

Target Template ◽

Template Update ◽

Local Sparse Representation

We develop a robust tracking method based on the structural local sparse representation and incremental subspace learning. This representation exploits both partial information and spatial information of the target. The similarity obtained by pooling across the local patches helps locate the target more accurately. In addition, we develop a template update method which combines incremental subspace learning and sparse representation. This strategy adapts the template to the appearance change of the target with less drifting and reduces the influence of the occluded target template as well. Experiments on challenging sequences with evaluation of the state-of-the-art methods show effectiveness of the proposed algorithm.

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