2D Person Tracking Using Kalman Filtering and Adaptive Background Learning in a Feedback Loop

摘要

This paper proposes a system for tracking people in video streams, returning their body and head bounding boxes. 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 is suitable for indoors and outdoors scenes with varying background and overcomes the problem of stationary targets fading into the background, commonly found in variations of Stauffer's adaptive background algorithm.