Particles coupled with Data Fusion for 3D Tracking

Robustness and tracking speed are two important indices to evaluate the performance of real-time 3D tracking. In this paper, we propose a new method to fuse sensing data of the most current observation into a 3D visual tracker with particle techniques. With the proposed approach, the importance density function in particle filter can be modified to represent posterior states by particle crowds in a better way. Thus, it makes the tracking system more robust to noise and outliers. On the other hand, because the particle interpretation is performed in a much more efficient fashion, the number of particles used in tracking is greatly reduced, which improves the real-time performance of the system. Simulation and experimental results verified the effectiveness of the proposed method.