title: Modeling and classifying human activities from trajectories using a class of space-varying parametric motion fields.
abstract: 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.
authors: Instituto de Sistemas e Robótica, Instituto Superior Técnico, Lisboa 1049-001, Portugal. jan@isr.ist.utl.pt
authors: Jacinto C
authors: JC
authors: Nascimento
authors: Jorge S
authors: JS
authors: Marques
authors: João M
authors: JM
authors: Lemos
mesh_terms: D000203:Activities of Daily Living / Q000145:classification*; D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D059629:Signal-To-Noise Ratio; D014741:Video Recording / Q000379:methods*