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2010

Trajectory Classification Using Switched Dynamical Hidden Markov Models

8 years 4 months ago
Trajectory Classification Using Switched Dynamical Hidden Markov Models
This paper proposes an approach for recognizing human activities (more specifically, pedestrian trajectories) in video sequences, in a surveillance context. A system for automatic processing of video information for surveillance purposes should be capable of detecting, recognizing, and collecting statistics of human activity, reducing human intervention as much as possible. In the method described in this paper, human trajectories are modeled as a concatenation of segments produced by a set of low level dynamical models. These low level models are estimated in an unsupervised fashion, based on a finite mixture formulation, using the expectation-maximization (EM) algorithm; the number of models is automatically obtained using a minimum message length (MML) criterion. This leads to a parsimonious set of models tuned to the complexity of the scene. We describe the switching among the low-level dynamic models by a hidden Markov chain; thus, the complete model is termed a switched dynamical...
Jacinto C. Nascimento, Mário A. T. Figueire
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TIP
Authors Jacinto C. Nascimento, Mário A. T. Figueiredo, Jorge S. Marques
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