Algorithm and architecture for object tracking using particle filter
Particle filtering is a popular algorithm for vision-based target tracking. Despite its effectiveness in many fields of tracking, however, the computation requirement of particle filters is high. In this paper we propose an algorithm and architecture for vision-based particle filters. The proposed algorithm can estimate objects' positions, sizes, and angles by using color histogram as the feature. We propose two hardware parallelization schemes — one makes use of particle-level parallel operation and the other utilizes particle-level plus pixel-level parallel operation. The experimental result shows the tracking accuracy of our test sequences is over 85%, and the proposed hardware architecture can run the particle filtering in real-time, with 31.35 frames per second in average.