Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.
|Title of host publication||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|Publication status||Published - 2015 Oct 14|
|Event||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States|
Duration: 2015 Jun 7 → 2015 Jun 12
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Other||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015|
|Period||15/6/7 → 15/6/12|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition