Adaptive Ball Particle Filter and its Application to Visual Tracking
Xia Yu, Xiao‐Jun Wu · IETE Technical Review · 2015
To improve the quality of state-space exploration and the performance of object tracking, this paper proposes a new adaptive ball particle filter for robust visual tracking. The proposed algorithm guarantees the valid particles in propagation of particle filter using an innovative ball sampling mode. In contrast to conventional approaches, the proposed algorithm uses much fewer particles to ameliorate the diversity of distribution, and effectively overcomes the particle degeneration problem. By the iterative movement of the ball between successive frames, particles move towards the regions with higher values of the posterior density function. Furthermore, the number of particles is estimated adaptively by the tracked object and background. Theoretical analysis and simulated experiments show the superiority of the proposed method.