TY - GEN
T1 - Boosting-based visual tracking using structural local sparse descriptors
AU - Liu, Yangbiao
AU - Ma, Bo
AU - Hu, Hongwei
AU - Han, Yin
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - This paper develops an online algorithm based on sparse representation and boosting for robust object tracking. Local descriptors of a target object are represented by pooling some sparse codes of its local patches, and an Adaboost classifier is learned using the local descriptors to discriminate target from background. Meanwhile, the proposed algorithm assigns a weight value, calculated with the generativemodel, to each candidate object to adjust the classification result. In addition, a template update strategy, based on incremental principal component analysis and occlusion handing scheme, is presented to capture the appearance change of the target and to alleviate the visual drift problem. Comparison with the state-of-the-art trackers on the comprehensive benchmark shows effectiveness of the proposed method.
AB - This paper develops an online algorithm based on sparse representation and boosting for robust object tracking. Local descriptors of a target object are represented by pooling some sparse codes of its local patches, and an Adaboost classifier is learned using the local descriptors to discriminate target from background. Meanwhile, the proposed algorithm assigns a weight value, calculated with the generativemodel, to each candidate object to adjust the classification result. In addition, a template update strategy, based on incremental principal component analysis and occlusion handing scheme, is presented to capture the appearance change of the target and to alleviate the visual drift problem. Comparison with the state-of-the-art trackers on the comprehensive benchmark shows effectiveness of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=84929617349&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16814-2_34
DO - 10.1007/978-3-319-16814-2_34
M3 - Conference contribution
AN - SCOPUS:84929617349
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 522
EP - 533
BT - Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Cremers, Daniel
A2 - Saito, Hideo
A2 - Reid, Ian
A2 - Yang, Ming-Hsuan
PB - Springer Verlag
T2 - 12th Asian Conference on Computer Vision, ACCV 2014
Y2 - 1 November 2014 through 5 November 2014
ER -