IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Collaborative Kernel Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Density-Based Appearance Modeling for Object Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object tracking with dynamic feature graph
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Representing Images Using Nonorthogonal Haar-Like Bases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft-assigned bag of features tracking
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Co-trained generative and discriminative trackers with cascade particle filter
Computer Vision and Image Understanding
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Visual tracking is one of the central problems in computer vision. A crucial problem of tracking is how to represent the object. Traditional appearance-based trackers are using increasingly more complex features in order to be robust. However, complex representations typically will not only require more computation for feature extraction, but also make the state inference complicated. In this paper, we show that with a careful feature selection scheme, extremely simple yet discriminative features can be used for robust object tracking. The central component of the proposed method is a succinct and discriminative representation of image template using discriminative non-orthogonal binary subspace spanned by Haar-like features. These Haar-like bases are selected from the over-complete dictionary using a variation of the OOMP (optimized orthogonal matching pursuit). Such a representation inherits the merits of original NBS in that it can be used to efficiently describe the object. It also incorporates the discriminative information to distinguish the foreground and background. We apply the discriminative NBS to object tracking through SSD-based template matching. An update scheme of the discriminative NBS is devised in order to accommodate object appearance changes. We validate the effectiveness of our method through extensive experiments on challenging videos and demonstrate its capability to track objects in clutter and moving background.