Approximate Bayesian Multibody Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary fragment matching and articulated pose under occlusion
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
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Statistical models for tracking different moving bodies must be able to reason about occlusions in order to be effective. Representing the joint statistics across different bodies is computationally hard, since the size of the representation grows exponentially with the number of bodies being tracked. Separable tracking, with one tracker per body, cannot deal with occlusions effectively. We propose a new model, dubbed Hybrid Joint-Separable (HJS), that uses a representation size that grows linearly with the number of bodies, and a computational complexity that grows quadratically. This model can reason explicitly about occlusions. We describe a particle filter implementation of this model, and present promising experimental results.