Visual tracking via adaptive structural local sparse appearance model

Sparse representation has been applied to visual tracking by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based trackers only consider the holistic representation and do not make full use of the sparse coefficients to discrim...

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Vydáno v:2012 IEEE Conference on Computer Vision and Pattern Recognition s. 1822 - 1829
Hlavní autoři: Xu Jia, Huchuan Lu, Ming-Hsuan Yang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2012
Témata:
Adaptation models
Dictionaries
Mathematical model
Robustness
Target tracking
Vectors
Visualization
ISBN:9781467312264, 1467312266
ISSN:1063-6919, 1063-6919
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Shrnutí:Sparse representation has been applied to visual tracking by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based trackers only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the target and the background, and hence may fail with more possibility when there is similar object or occlusion in the scene. In this paper we develop a simple yet robust tracking method based on the structural local sparse appearance model. This representation exploits both partial information and spatial information of the target based on a novel alignment-pooling method. The similarity obtained by pooling across the local patches helps not only locate the target more accurately but also handle occlusion. In addition, we employ a template update strategy which combines incremental subspace learning and sparse representation. This strategy adapts the template to the appearance change of the target with less possibility of drifting and reduces the influence of the occluded target template as well. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.
ISBN:9781467312264
1467312266
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2012.6247880