Multi-agent based particle filter for moving object tracking

When tracking the moving targets in video image sequences with the existing particle filter, usually the tracking performance is not satisfactory due to the particle degradation and particle diversity loss. In this paper, we propose a novel particle filtering algorithm. In the algorithm, the multi-agent co-evolutionary mechanism is introduced into the particle re-sampling process and make the particle become an agent having ability of local perception, competitive selection and self-learning by the redefinition of particle agent and its local living environment. The re-sampling process is accomplished by the co-evolutionary behaviors among particles such as competition, crossover, mutation and self-learning, etc. It can not only ensure the particle validity but also increase the particle diversity. Experimental results show that the proposed algorithm can achieve better performance when tracking objects in complex video scenes.