Dynamic View Planning by Effective Particles for Three-Dimensional Tracking

In this paper, we propose a new approach to dynamically manage the viewpoint of a vision system for optimal 3-D tracking using particle techniques. We adopt the effective sample size in the proposed particle filter as a criterion for evaluating tracking performance and employ it to guide the view-planning process for finding the best viewpoint configuration. In our approach, the vision system is designed and configured to achieve the largest number of effective particles, which minimizes tracking error by revealing the system to a better swarm of importance samples and interpreting posterior states in a better way. Superiorities of our method are shown by comparison with the resampling particle filter and other view-planning methods.