Robust object tracking using the particle filtering and level set methods: A comparative experiment

Robust visual tracking has become an important topic of research in computer vision. A novel method for robust object tracking, GATE [11], improves object tracking in complex environments using the particle filtering and the level set-based active contour method. GATE creates a spatial prior in the state space using shape information of the tracked object to filter particles in the state space in order to reshape and refine the posterior distribution of the particle filtering. This paper describes a comparative experiment that applies GATE and the standard particle filtering to track the object of interest in complex environments using simple features. Image sequences captured by the hand held, stationary and the PTZ camera are utilised. The experimental results demonstrate that GATE is able to solve the ambiguous outlier problem of particle filters in order to deal with heavy clutters in the background, occlusion, low resolution and noisy images, and thus significantly improves the particle filtering in object tracking.