Object tracking by occlusion detection via structured sparse learning

Tianzhu Zhang, Bernard Ghanem, Changsheng Xu, Narendra Ahuja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object's track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013
Pages1033-1040
Number of pages8
DOIs
StatePublished - Oct 8 2013
Event2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013 - Portland, OR, United States
Duration: Jun 23 2013Jun 28 2013

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Other

Other2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013
CountryUnited States
CityPortland, OR
Period6/23/136/28/13

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Zhang, T., Ghanem, B., Xu, C., & Ahuja, N. (2013). Object tracking by occlusion detection via structured sparse learning. In Proceedings - 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013 (pp. 1033-1040). [6595996] (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). https://doi.org/10.1109/CVPRW.2013.150