Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/231784
Title: Temporal Restricted Visual Tracking Via Reverse-Low-Rank Sparse Learning
Authors: Yehui Yang;Wenrui Hu;Yuan Xie;Wensheng Zhang;Tianzhu Zhang
Year: 2017
Publisher: IEEE
Abstract: An effective representation model, which aims to mine the most meaningful information in the data, plays an important role in visual tracking. Some recent particle-filter-based trackers achieve promising results by introducing the low-rank assumption into the representation model. However, their assumed low-rank structure of candidates limits the robustness when facing severe challenges such as abrupt motion. To avoid the above limitation, we propose a temporal restricted reverse-low-rank learning algorithm for visual tracking with the following advantages: 1) the reverse-low-rank model jointly represents target and background templates via candidates, which exploits the low-rank structure among consecutive target observations and enforces the temporal consistency of target in a global level; 2) the appearance consistency may be broken when target suffers from sudden changes. To overcome this issue, we propose a local constraint via 11,2 mixed-norm, which can not only ensures the local consistency of target appearance, but also tolerates the sudden changes between two adjacent frames; and 3) to alleviate the inference of unreasonable representation values due to outlier candidates, an adaptive weighted scheme is designed to improve the robustness of the tracker. By evaluating on 26 challenge video sequences, the experiments show the effectiveness and favorable performance of the proposed algorithm against 12 state-of-the-art visual trackers.
URI: http://localhost/handle/Hannan/231784
volume: 47
issue: 2
More Information: 485,
498
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7396944.pdf2.33 MBAdobe PDF
Title: Temporal Restricted Visual Tracking Via Reverse-Low-Rank Sparse Learning
Authors: Yehui Yang;Wenrui Hu;Yuan Xie;Wensheng Zhang;Tianzhu Zhang
Year: 2017
Publisher: IEEE
Abstract: An effective representation model, which aims to mine the most meaningful information in the data, plays an important role in visual tracking. Some recent particle-filter-based trackers achieve promising results by introducing the low-rank assumption into the representation model. However, their assumed low-rank structure of candidates limits the robustness when facing severe challenges such as abrupt motion. To avoid the above limitation, we propose a temporal restricted reverse-low-rank learning algorithm for visual tracking with the following advantages: 1) the reverse-low-rank model jointly represents target and background templates via candidates, which exploits the low-rank structure among consecutive target observations and enforces the temporal consistency of target in a global level; 2) the appearance consistency may be broken when target suffers from sudden changes. To overcome this issue, we propose a local constraint via 11,2 mixed-norm, which can not only ensures the local consistency of target appearance, but also tolerates the sudden changes between two adjacent frames; and 3) to alleviate the inference of unreasonable representation values due to outlier candidates, an adaptive weighted scheme is designed to improve the robustness of the tracker. By evaluating on 26 challenge video sequences, the experiments show the effectiveness and favorable performance of the proposed algorithm against 12 state-of-the-art visual trackers.
URI: http://localhost/handle/Hannan/231784
volume: 47
issue: 2
More Information: 485,
498
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7396944.pdf2.33 MBAdobe PDF
Title: Temporal Restricted Visual Tracking Via Reverse-Low-Rank Sparse Learning
Authors: Yehui Yang;Wenrui Hu;Yuan Xie;Wensheng Zhang;Tianzhu Zhang
Year: 2017
Publisher: IEEE
Abstract: An effective representation model, which aims to mine the most meaningful information in the data, plays an important role in visual tracking. Some recent particle-filter-based trackers achieve promising results by introducing the low-rank assumption into the representation model. However, their assumed low-rank structure of candidates limits the robustness when facing severe challenges such as abrupt motion. To avoid the above limitation, we propose a temporal restricted reverse-low-rank learning algorithm for visual tracking with the following advantages: 1) the reverse-low-rank model jointly represents target and background templates via candidates, which exploits the low-rank structure among consecutive target observations and enforces the temporal consistency of target in a global level; 2) the appearance consistency may be broken when target suffers from sudden changes. To overcome this issue, we propose a local constraint via 11,2 mixed-norm, which can not only ensures the local consistency of target appearance, but also tolerates the sudden changes between two adjacent frames; and 3) to alleviate the inference of unreasonable representation values due to outlier candidates, an adaptive weighted scheme is designed to improve the robustness of the tracker. By evaluating on 26 challenge video sequences, the experiments show the effectiveness and favorable performance of the proposed algorithm against 12 state-of-the-art visual trackers.
URI: http://localhost/handle/Hannan/231784
volume: 47
issue: 2
More Information: 485,
498
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7396944.pdf2.33 MBAdobe PDF