Online Nonparametric Bayesian Activity Mining and Analysis from Surveillance Video
| Autor: | Vahid Bastani, Carlo S. Regazzoni, Lucio Marcenaro |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Computer science
abnormality detection 02 engineering and technology Dirichlet process mixture nonparametric Bayesian symbols.namesake Kriging 0202 electrical engineering electronic engineering information engineering Cluster analysis Gaussian process Block (data storage) online activity analysis business.industry state dynamics learning 020206 networking & telecommunications Pattern recognition Object (computer science) Computer Graphics and Computer-Aided Design ComputingMethodologies_PATTERNRECOGNITION Flow (mathematics) symbols Incremental trajectory clustering 020201 artificial intelligence & image processing Software Artificial intelligence business Particle filter |
| Popis: | A method for online incremental mining of activity patterns from the surveillance video stream is presented in this paper. The framework consists of a learning block in which Dirichlet process mixture model is employed for the incremental clustering of trajectories. Stochastic trajectory pattern models are formed using the Gaussian process regression of the corresponding flow functions. Moreover, a sequential Monte Carlo method based on Rao-Blackwellized particle filter is proposed for tracking and online classification as well as the detection of abnormality during the observation of an object. Experimental results on real surveillance video data are provided to show the performance of the proposed algorithm in different tasks of trajectory clustering, classification, and abnormality detection. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|