Activity Recognition Using the Dynamics of the Configuration of Interacting Objects

Monitoring activities using video data is an important surveillance problem. A special scenario is to learn the pattern of normal activities and detect abnormal events from a very low resolution video where the moving objects are small enough to be modeled as point objects in a 2D plane. Instead of tracking each point separately, we propose to model an activity by the polygonal `shape' of the configuration of these point masses at any time ? , and its deformation over time. We learn the mean shape and the dynamics of the shape change using hand-picked location data (no observation noise) and define an abnormality detection statistic for the simple case of a test sequence with negligible observation noise. For the more practical case where observation (point locations) noise is large and cannot be ignored, we use a particle filter to estimate the probability distribution of the shape given the noisy observations upto the current time. Abnormality detection in this case is formulat...

• Type: Conference
• Year: 2003
• Where: CVPR
• Authors: Namrata Vaswani, Amit K. Roy Chowdhury, Rama Chellappa
• Keywords: Abnormality Detection, Change Detection Problem, Computer Vision, CVPR 2003, Mean Shape, Negligible Observation Noise, Shape Change