Using adaptive tracking to classify and monitor activities in a site

Abstract

We describe a vision system that monitors activity in a site over extended periods of time. The system uses a distributed set of sensors to cover the site, and an adaptive tracker detects multiple moving objects in the sensors. Our hypothesis is that motion tracking is sufficient to support a range of computations about site activities. We demonstrate using the tracked motion data to calibrate the distributed sensors, to construct rough site models, to classify detected objects, to learn common patterns of activity for different object classes, and to detect unusual activities.

Keywords

Computer scienceTracking (education)Artificial intelligenceComputer vision

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Publication Info

Year: 2002
Type: article
Citations: 504
Access: Closed

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APA Style

                            
                                    W. Eric L. Grimson, 
                                
                                    Chris Stauffer, 
                                
                                    R. Romano
                                
                                et al.
                            
                            (2002). 
                            Using adaptive tracking to classify and monitor activities in a site. 
                            Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231)
                            
                            .
                            https://doi.org/10.1109/cvpr.1998.698583

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DOI: 10.1109/cvpr.1998.698583

Data Quality

Data completeness: 77%