Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge Conditions
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust Multiperson Tracking from a Mobile Platform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cascaded confidence filtering for improved tracking-by-detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A stochastic graph evolution framework for robust multi-target tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally-optimal greedy algorithms for tracking a variable number of objects
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
How does person identity recognition help multi-person tracking?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Multi-target tracking by continuous energy minimization
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Learning affinities and dependencies for multi-target tracking using a CRF model
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Image Processing
Discrete-continuous optimization for multi-target tracking
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
An online learned CRF model for multi-target tracking
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Robust visual tracking via multi-task sparse learning
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Tracking multiple people under global appearance constraints
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Online learning of linear predictors for real-time tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Online learned discriminative part-based appearance models for multi-human tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
GMCP-Tracker: global multi-object tracking using generalized minimum clique graphs
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Group tracking: exploring mutual relations for multiple object tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Real-time compressive tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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We introduce an online learning approach for multi-target 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 tracking targets in presence of camera motions. An efficient algorithm is introduced for finding an association with low energy cost. We present results on four public data sets, and show significant improvements compared with several state-of-art methods.