Closed-Loop Tracking and Change Detection in Multi-Activity Sequences

We present a novel framework for tracking of a long sequence of human activities, including the time instances of change from one activity to the next, using a closed-loop, non-linear dynamical feedback system. A composite feature vector describing the shape, color and motion of the objects, and a non-linear, piecewise stationary, stochastic dynamical model describing its spatio-temporal evolution, are used for tracking. The tracking error or expected log likelihood, which serves as a feedback signal, is used to automatically detect changes and switch between activities happening one after another in a long video sequence. Whenever a change is detected, the tracker is reinitialized automatically by comparing the input image with learned models of the activities. Unlike some other approaches that can track a sequence of activities, we do not need to know the transition probabilities between the activities, which can be difficult to estimate in many application scenarios. We demonstrate...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Bi Song, Namrata Vaswani, Amit K. Roy Chowdhury
• Keywords: Composite Feature Vector, Computer Vision, CVPR 2007, Long Sequence, Long Video Sequence, Non-linear Dynamical Feedback, Stochastic Dynamical Model