TY - GEN
T1 - Patchwise tracking via spatio-temporal constraint-based sparse representation and multiple-instance learning-based SVM
AU - Wang, Yuxia
AU - Zhao, Qingjie
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - This paper proposes a patch-based tracking algorithm via a hybrid generative-discriminative appearance model. For establishing the generative appearance model, we present a spatio-temporal constraintbased sparse representation (STSR), which not only exploits the intrinsic relationship among the target candidates and the spatial layout of the patches inside each candidate, but also preserves the temporal similarity in consecutive frames. To construct the discriminative appearance model, we utilize the multiple-instance learning-based support vector machine (MIL&SVM), which is robust to occlusion and alleviates the drifting problem. According to the classification result, the occlusion state can be predicted, and it is further used in the templates updating, making the templates more efficient both for the generative and discriminative model. Finally, we incorporate the hybrid appearance model into a particle filter framework. Experimental results on six challenging sequences demonstrate that our tracker is robust in dealing with occlusion.
AB - This paper proposes a patch-based tracking algorithm via a hybrid generative-discriminative appearance model. For establishing the generative appearance model, we present a spatio-temporal constraintbased sparse representation (STSR), which not only exploits the intrinsic relationship among the target candidates and the spatial layout of the patches inside each candidate, but also preserves the temporal similarity in consecutive frames. To construct the discriminative appearance model, we utilize the multiple-instance learning-based support vector machine (MIL&SVM), which is robust to occlusion and alleviates the drifting problem. According to the classification result, the occlusion state can be predicted, and it is further used in the templates updating, making the templates more efficient both for the generative and discriminative model. Finally, we incorporate the hybrid appearance model into a particle filter framework. Experimental results on six challenging sequences demonstrate that our tracker is robust in dealing with occlusion.
KW - Hybrid generative-discriminative appearance model
KW - MIL&SVM
KW - Patchwise tracking
KW - Sparse representation
KW - Spatio-temporal constraint
UR - http://www.scopus.com/inward/record.url?scp=84952792640&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-26532-2_29
DO - 10.1007/978-3-319-26532-2_29
M3 - Conference contribution
AN - SCOPUS:84952792640
SN - 9783319265315
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 264
EP - 271
BT - Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
A2 - Lai, Weng Kin
A2 - Liu, Qingshan
A2 - Huang, Tingwen
A2 - Arik, Sabri
PB - Springer Verlag
T2 - 22nd International Conference on Neural Information Processing, ICONIP 2015
Y2 - 9 November 2015 through 12 November 2015
ER -