Using a Light DBSCAN Algorithm for Visual Surveillance of Crowded Traffic Scenes
| Autor: | Amar El Maadi, Mohand Saïd Djouadi |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
DBSCAN
Motion analysis business.industry Computer science Perspective (graphical) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Optical flow Tracking (particle physics) Motion (physics) Computer Science Applications Theoretical Computer Science Eye tracking Computer vision Artificial intelligence Electrical and Electronic Engineering business Cluster analysis |
| Zdroj: | IETE Journal of Research. 61:308-320 |
| ISSN: | 0974-780X 0377-2063 |
| DOI: | 10.1080/03772063.2015.1017614 |
| Popis: | Behaviour analysis in visual surveillance has become a very active issue for the computer vision research community, particularly when crowded scenes are concerned. In this perspective, motion analysis and tracking is challenging due to significant visual ambiguities which incite to look into more alternative solutions. In this work, we introduce a new framework for recognizing various motion patterns, extracting abnormal behaviours, and tracking them over crowded traffic scenes. The proposed approach exploits a novel density-based clustering method and highlights three traffic density levels. It performs in two modes: an “offline” mode for motion patterns learning and modelling, and an “online” mode for distinguishing irregular motions and tracking them separately. We developed a light density-based clustering technique to “online” cluster motion vectors, produced by optical flow, and compare them with motion pattern models previously defined. Non-identified clusters are treated as suspicious and... |
| Databáze: | OpenAIRE |
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