Swarming particles with multi-feature model for free-selected object tracking

This paper presents a new object tracking algorithm that embeds swarming particles into generic particle filter framework to achieve more robustness and flexibility. Firstly a group of particles associated with potential solutions are initialized in a high-dimensional space. Then particle swarm optimization (PSO) is used to drive particles flying. The object is tracked when the particles reach convergence. This PSO-based algorithm contains resample, similarity measure, and integration together such that the degeneracy problem of particle filter can be avoided. Furthermore, a multiple feature model is proposed for object description to enhance the tracking accuracy and efficiency. The proposed algorithm is independent with specific objects and can be used for any free-selected object tracking. Some experimental results demonstrate efficiency and robustness of the algorithm.