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A bat-inspired particle filter for visual tracking

Particle filters perform the nonlinear estimation and have been proven to be a powerful tool in solving visual tracking problems. However, the problem of sample impoverishment is still a drawback of particle filter. To solve this problem, a bat-inspired particle filter is proposed in this work. The particles in the particle filter are optimized using a new biologically inspired optimization algorithm, namely bat algorithm before re-sampling. The meaningful particles are increased and they can approximate the true state of the target more accurately. Experiments on visual tracking show that the proposed algorithm outperforms the standard particle filter and it can track targets accurately and robustly in various challenging conditions.