Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Covariance Tracking using Model Update Based on Lie Algebra
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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 and fast collaborative tracking with two stage sparse optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Robust tracking using local sparse appearance model and K-selection
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
Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise
IEEE Transactions on Information Theory
Visual tracking via adaptive structural local sparse appearance model
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Visual tracking is an important task in many computer vision applications. In this paper, we present a novel approach for visual tracking using a local sparse appearance model that exploits spatial information. The sparse information along with the spatial information of local patches within this representation is used to determine the motion of the object. When the match between the target and candidate patches are represented in matrix form, the translation motion of the object can be obtained by analyzing the diagonals of the mapping matrix. A novel local template update strategy is proposed to update the relevant parts of the object within the candidate undergoing changes. Along with the local patch update, we use an adaptive/permanent template update strategy which gives less update priority to the transient local patches accounting for partial occlusion. This approach performs comparably against state-of-the-art tracking techniques, in various challenging videos involving changes in scale, pose, illumination and partial occlusion. The proposed approach tracks at a processing speed of over 40 frames per second and it is suitable for various real-time applications.