ACM Computing Surveys (CSUR)
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Tracking and Vehicle Classification via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a discriminative dictionary for sparse coding via label consistent K-SVD
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Robust sparse coding for face recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Minimum error bounded efficient $/ell _1$ tracker with occlusion detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Information Theory
Fisher Discrimination Dictionary Learning for sparse representation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Video object tracking plays an important role in modern computer vision, and many algorithms have been proposed in recent years. ℓ1-tracker, a novel generative tracking method based on sparse coding, has demonstrated very promising performance in numerous challenging sequences. But the high computational cost, which is caused by the large size of dictionary, influences its application in tracking severely. In this paper, based on original Fisher discriminant dictionary learning(FDDL) and our improved version, we present a novel tracking algorithm, called FD2LT. In our framework, tracking is considered as a problem consisting of three components, including object location, training samples selection, and dictionary updating. Experimental results demonstrate the effectiveness of the proposed tracking algorithm.