Multiple-Hypothesis Chance-Constrained Target Tracking Under Identity Uncertainty

We propose a robust target tracking algorithm for a mobile robot under identity uncertainty, which arises in crowded environments. When a mobile robot has a sensor with a fan-shaped field of view and finite sensing region, the proposed algorithm aims to minimize the probability of losing a moving target. We predict the next position of a moving target in a crowded environment using a multiple-hypothesis prediction algorithm which combines the motion model and appearance model of the target. When the distribution of the target's next position follows a Gaussian mixture model, the proposed tracking algorithm can track a target with a guaranteed tracking success probability. If the tracking success probability is sufficiently good, the method minimizes the moving distance of the mobile robot. The performance of the method is extensively validated in simulation and experiments using a Pioneer robot with a Microsoft Kinect sensor.