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Online shared dictionary learning for visual tracking

Published: 19 August 2015 Publication History

Abstract

Due to the superior representation ability and robustness to noise, sparse representation has been applied to visual tracking by many researchers. However, the dictionary learning strategies of previous methods suffer from the difficulty of seeking a balance of reconstructive and discriminative abilities of the learned dictionary. In this work, we propose to learn a shared dictionary in addition to the target and background specific dictionaries for robust visual tracking. With the shared dictionary modeling the commonality between the target and background, and specific dictionaries capturing the difference, our learned dictionary is both reconstructive and discriminative which can better distinguish the target from the background. The best candidate is selected as the tracking result based on the reconstruction error and discriminative ability. Experimental results on eight public challenging video sequences demonstrate our proposed algorithm outperforms eight state-of-the-art trackers.

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cover image ACM Other conferences
ICIMCS '15: Proceedings of the 7th International Conference on Internet Multimedia Computing and Service
August 2015
397 pages
ISBN:9781450335287
DOI:10.1145/2808492
  • General Chairs:
  • Ramesh Jain,
  • Shuqiang Jiang,
  • Program Chairs:
  • John Smith,
  • Jitao Sang,
  • Guohui Li
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 August 2015

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  1. dictionary learning
  2. sparse representation
  3. target tracking

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ICIMCS '15

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ICIMCS '15 Paper Acceptance Rate 20 of 128 submissions, 16%;
Overall Acceptance Rate 163 of 456 submissions, 36%

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