IEEE Trans Image Process - Modeling and classifying human activities from trajectories using a class of space-varying parametric motion fields.

Tópicos

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Resumo

Many approaches to trajectory analysis, such as clustering or classification, use probabilistic generative models, thus not requiring trajectory alignment/registration. Switched linear dynamical models (e.g., HMMs) have been used in this context, due to their ability to describe different motion regimes. However, these models are not suitable for handling space-dependent dynamics that are more naturally captured by nonlinear models. As is well known, these are more difficult to identify. In this paper, we propose a new way of modeling trajectories, based on a mixture of parametric motion vector fields that depend on a small number of parameters. Switching among these fields follows a probabilistic mechanism, characterized by a field of stochastic matrices. This approach allows representing a wide variety of trajectories and modeling space-dependent behaviors without using global nonlinear dynamical models. Experimental evaluation is conducted in both synthetic and real scenarios. The latter concerning with human trajectory modeling for activity classification, a central task in video surveillance.

Resumo Limpo

mani approach trajectori analysi cluster classif use probabilist generat model thus requir trajectori alignmentregistr switch linear dynam model eg hmms use context due abil describ differ motion regim howev model suitabl handl spacedepend dynam natur captur nonlinear model well known difficult identifi paper propos new way model trajectori base mixtur parametr motion vector field depend small number paramet switch among field follow probabilist mechan character field stochast matric approach allow repres wide varieti trajectori model spacedepend behavior without use global nonlinear dynam model experiment evalu conduct synthet real scenario latter concern human trajectori model activ classif central task video surveil

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