Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
A Sampling Algorithm for Tracking Multiple Objects
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Decentralized Multiple Target Tracking Using Netted Collaborative Autonomous Trackers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Simultaneous Estimation of Segmentation and Shape
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
An integrated Monte Carlo data association framework for multi-object tracking
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Tracking Multiple Visual Targets via Particle-Based Belief Propagation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We present a hierarchical and compositional model based on an And-or graph for joint detecting and tracking of multiple targets in video. In the graph, an And-node for the joint state of all targets is decomposed into multiple Or-nodes. Each Or-node represents an individual target's state that includes position, appearance, and scale of the target. Leaf nodes are trained detectors. Measurements that supplied by the predictions of the tracker and leaf nodes are shared among Or-nodes.There are two kinds of production rules respectively designed for the problems of varying number and occlusions. One is association relations that distributes measurements to targets, and the other is semantic relations that represent occlusion between targets. The inference algorithm for the graph consists of three processing channels: (1) a bottom-up channel, which provides informative measurements by using learned detectors; (2) a top-down channel, which estimates the individual target state with joint probabilistic data association; (3) a context sensitive reasoning channel, which finalizes the estimation of the joint state with belief propagation. Additionally, an interaction mechanism between detection and tracking is implemented by a hybrid measurement process. The algorithm is validated widely by tracking peoples in several complex scenarios. Empirical results show that our tracker can reliably track multi-target without any prior knowledge about the number of targets and the targets may appear or disappear anywhere in the image frame and at any time in all these test videos.