Distance Transform Based Gaussian Distribution for Probabilistic Target Tracking

In this paper, a promising approach for visual target tracking is presented. A mobile robot should keep an unpredictably moving target in a cluttered environment within its field of view. The objective is mainly to plan online the shortest tracking path that offers a continuous monitoring of the target. A distance transform based Gaussian distribution for obstacle cluttered space is used to build a probabilistic model for the target future location. It identifies the target potential escapes out of the robot view. Accordingly, the robot navigates to an appropriate observing point that is calculated and updated continuously as the target moves. The approach has been tested on simulated static and dynamic environments. A number of test cases are presented