Human tracking from a mobile agent: Optical flow and Kalman filter arbitration

Tracking moving objects is one of the most important but problematic features of motion analysis and understanding. The Kalman filter (KF) has commonly been used for estimation and prediction of the target position in succeeding frames. In this paper, we propose a novel and efficient method of tracking, which performs well even when the target takes a sudden turn during its motion. The proposed method arbitrates between KF and Optical flow (OF) to improve the tracking performance. Our system utilizes a laser to measure the distance to the nearest obstacle and an infrared camera to find the target. The relative data is then fused with the Arbitrate OFKF filter to perform real-time tracking. Experimental results show our suggested approach is very effective and reliable for estimating and tracking moving objects.

► An autonomous mobile robot is designed to track a human using an infrared camera.
► The proposed method arbitrates between Kalman filter and Optical flow to improve the tracking performance.
► The algorithm has been successfully tested in real-time tracking of a man in an indoor lab environment.
► Compare the results to other approaches such as particle filters.