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.

Assessing Temporal Coherence for Posture Classification with Large Occlusions / Cucchiara, Rita; Vezzani, Roberto. - STAMPA. - 2:(2005), pp. 269-274. ((Intervento presentato al convegno IEEE Computer Society Workshop on Motion and Video Computing tenutosi a Breckenridge, Colorado nel 5-7 January 2005.

Assessing Temporal Coherence for Posture Classification with Large Occlusions

CUCCHIARA, Rita;VEZZANI, Roberto
2005

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.
IEEE Computer Society Workshop on Motion and Video Computing
Breckenridge, Colorado
5-7 January 2005
2
269
274
Cucchiara, Rita; Vezzani, Roberto
Assessing Temporal Coherence for Posture Classification with Large Occlusions / Cucchiara, Rita; Vezzani, Roberto. - STAMPA. - 2:(2005), pp. 269-274. ((Intervento presentato al convegno IEEE Computer Society Workshop on Motion and Video Computing tenutosi a Breckenridge, Colorado nel 5-7 January 2005.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/464367
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