Robust visual tracking based on product sparse coding

Huang, Hongtu; Du-yan, BI; Zha, Yufei; Ma, Shiping; Gao, Shan; Liu, Chang

doi:10.1016/j.patrec.2015.01.014

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…The tracking algorithms based on SC or representation show state-of-the-art performance, as reported previously [24]. In general, these existing trackers can be divided into two categories: i. SC-based target appearance modelling [25][26][27][28][29]: the essence of this category is that SC is used for feature representation. ii.…”

Section: Sc For Tracking Tasksmentioning

confidence: 65%

“…The tracking algorithms based on SC or representation show state‐of‐the‐art performance, as reported previously [24]. In general, these existing trackers can be divided into two categories: SC‐based target appearance modelling [25–29]: the essence of this category is that SC is used for feature representation. Sparse representation‐based target searching [30–33]: the essence of this category is that a sparse representation classifier is used to discriminate the target from the background. …”

Section: Related Workmentioning

confidence: 89%

“…SC‐based target appearance modelling [25–29]: the essence of this category is that SC is used for feature representation.…”

Section: Related Workmentioning

confidence: 99%

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Visual tracking using locality‐constrained linear coding under a particle filtering framework

Ding

Wei

Cao

et al. 2017

IET Computer Vision

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Visual target tracking has long been a challenging problem because of the variable appearance of the target with changing spatiotemporal factors. Therefore, it is important to design an effective and efficient appearance model for tracking tasks. This study proposes a tracking algorithm based on locality-constrained linear coding (LLC) under a particle filtering framework. A local feature descriptor is presented that can evenly represent the local information of each patch in the tracking region. LLC uses the locality constraints to project each local feature descriptor into its local-coordinate system. Compared with sparse coding, LLC can be performed very quickly for appearance modelling because it has an analytical solution derived by a three-step matrix calculation, and the computational complexity of the proposed tracking algorithm is o(η × m × n). Both quantitative and qualitative experimental results demonstrate that the authors' proposed algorithm performs favourably against the 10 state-of-the-art trackers on 12 challenging test sequences. However, related experimental results show that the performance of their tracker is not effective enough for small tracking targets owing to a lack of sufficient local region information.

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Section: Sc For Tracking Tasksmentioning

confidence: 65%

Section: Related Workmentioning

confidence: 89%

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Visual tracking using locality‐constrained linear coding under a particle filtering framework

Ding

Wei

Cao

et al. 2017

IET Computer Vision

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“…However, our original intention is to reduce the computation cost of implementing particle filters which means less particles. Huang used ridge regression to delete the outlying particles to obtain fewer particles [ 9 ]. Combining the two different methods above, here, we use P ( c t , s t | Z t ) to reselect more suitable particles.…”

Section: High-order Particle Filtering With Combined Featuresmentioning

confidence: 99%

“…Nevertheless, another alternative way is to reduce the quantity of samples. Ridge regression was employed [ 9 ] to decrease the computational costs for it could exclude the outlying particles. Their product sparse coding guaranteed their lower calculation simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Visual tracking in high-dimensional particle filter

et al. 2018

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In this paper, we propose a novel object tracking algorithm by using high-dimensional particle filter and combined features. Firstly, the refined two-dimensional principal component analysis and the tendency are combined to represent an object. Secondly, we present a framework using high-order Monte Carlo Markov Chain which considers more information and performs more discriminative and efficient on moving objects than the traditional first-order particle filtering. Finally, an advanced sequential importance resampling is applied to estimate the posterior density and obtains the high-quality particles. To further gain the better samples, K-means clustering is used to select more typical particles, which reduces the computational cost. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the performance of our proposed algorithm is superior to the state-of-the-art methods.

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A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning

et al. 2019

Front. Comput. Sci.

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Robust visual tracking based on product sparse coding

Cited by 8 publications

References 29 publications

Visual tracking using locality‐constrained linear coding under a particle filtering framework

Visual tracking using locality‐constrained linear coding under a particle filtering framework

Visual tracking in high-dimensional particle filter

A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning

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