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 Workshop on Motion and Video Computing, MOTION 2005 tenutosi a Breckenridge, CO, usa nel 5-7 January 2005) [10.1109/ACVMOT.2005.22].

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.
2005
IEEE Workshop on Motion and Video Computing, MOTION 2005
Breckenridge, CO, usa
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 Workshop on Motion and Video Computing, MOTION 2005 tenutosi a Breckenridge, CO, usa nel 5-7 January 2005) [10.1109/ACVMOT.2005.22].
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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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