Kalman Tracking with Target Feedback on Adaptive Background Learning

This paper proposes novel algorithms and system architecture for tracking targets in video streams. The proposed system comprises a variation of Stauffer's adaptive background algorithm with spacio-temporal adaptation of the learning parameters and a Kalman tracker in a feedback configuration. In the feed-forward path, the adaptive background module provides target evidence to the Kalman tracker. In the feedback path, the Kalman tracker adapts the learning parameters of the adaptive background module. The proposed feedback architecture overcomes the problem of stationary targets fading into the background, commonly found in variations of Stauffer's adaptive background algorithm and is capable of automatic initialization without the need for an initial background image.