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Assessing Temporal Coherence for Posture Classification with Large Occlusions

Abstract: In this paper we present a people posture classificationapproach especially devoted to cope with occlusions. Inparticular, the approach aims at assessing temporal coherenceof visual data over probabilistic models. A mixed predictiveand probabilistic tracking is proposed: a probabilistictracking maintains along time the actual appearance ofdetected people and evaluates the occlusion probability; anadditional tracking with Kalman prediction improves the estimationof the people position inside the room. ProbabilisticProjection Maps (PPMs) created with a learning phaseare matched against the appearance mask of the track. Finally,an Hidden Markov Model formulation of the posturecorrects the frame-by-frame classification uncertainties andmakes the system reliable even in presence of occlusions.Results obtained over real indoor sequences are discussed.


Citation:

Cucchiara, Rita; Vezzani, Roberto "Assessing Temporal Coherence for Posture Classification with Large Occlusions" Proceedings of Motion 2005, vol. 2, Breckenridge, Colorado, pp. 269 -274 , 5-7 January 2005, 2005

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