Research on PSOGA particle filter video object tracking algorithm based on local multi-zone
Feng Xiao · 2016
Video object tracking, which has broad application prospects in many fields such as navigation guidance, video surveillance, human-computer interaction, medical diagnosis and so on, is a hot research direction in the field of computer vision. Aiming at the inherent particle degradation problem of particle filter algorithm and the particles scarcity problem leaded by resampling step, a particle swarm optimization genetic particle filter algorithm is proposed based on local multi-zone. The algorithm leads the sampling particles to high likelihood area using the particle swarm optimization, slowing down the particle weight degradation; Then, the algorithm increases the diversity of particles through genetic algorithm instead of the traditional resampling step, avoids algorithm falling into local optimum, enhances the global search ability of algorithm, thus relieves particles scarcity problem. The proposed algorithm merges the latest measurement information into the importance density function, which is made more close to the real posteriori probability distribution of the object. In addition, when the object is obscured, the proposed algorithm randomly selects local area as the particle state model, so that the particle state model can contain block information as little as possible, overcoming the barrier interfering problems effectively; At the same time, describing object by local area can reduce the amount of calculation and improve the instantaneity of algorithm.