Face association across unconstrained video frames using conditional random fields
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Identity inference: generalizing person re-identification scenarios
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Hierarchical feature grouping for multiple object segmentation and tracking
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Markov random fields for sketch based video retrieval
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Online multi-target tracking by large margin structured learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Multi-target tracking on confidence maps: An application to people tracking
Computer Vision and Image Understanding
Multiple human tracking system for unpredictable trajectories
Machine Vision and Applications
Multi-Target Tracking by Online Learning a CRF Model of Appearance and Motion Patterns
International Journal of Computer Vision
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We introduce an online learning approach for multitarget tracking. Detection responses are gradually associated into tracklets in multiple levels to produce final tracks. Unlike most previous approaches which only focus on producing discriminative motion and appearance models for all targets, we further consider discriminative features for distinguishing difficult pairs of targets. The tracking problem is formulated using an online learned CRF model, and is transformed into an energy minimization problem. The energy functions include a set of unary functions that are based on motion and appearance models for discriminating all targets, as well as a set of pairwise functions that are based on models for differentiating corresponding pairs of tracklets. The online CRF approach is more powerful at distinguishing spatially close targets with similar appearances, as well as in dealing with camera motions. An efficient algorithm is introduced for finding an association with low energy cost. We evaluate our approach on three public data sets, and show significant improvements compared with several state-of-art methods.