Using Adaptive Tracking to Classify and Monitor Activities in a Site

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 su cient 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 di erent object classes, and to detect unusual activities. 1 A motivating scenario Our goal is a vision system that monitors activity in a site over extended periods of time, i.e., patterns of motion and interaction demonstrated by objects in the site. The system should provide statistical descriptions of typical activity patterns, e.g., normal vehicular volume or normal pedestrian tra c paths for a given time of day it should detect unusual events, by spotting activitie...

• Type: Conference
• Year: 1998
• Where: CVPR
• Authors: W. Eric L. Grimson, Chris Stauffer, R. Romano, L. Lee
• Keywords: Computer Vision, CVPR 1998, Normal Patterns, Normal Pedestrian Tra, Normal Vehicular Volume, Scenario Our Goal, Typical Activity Patterns