An improved particle filter based on cuckoo search for visual tracking
Guixia Fu, Gao Mingliang, Zou Guo-Feng, Liu Wen-can, Liu Li-Na · 2018
Particle filter (PF) has been proven to be a powerful tool to solve visual tracking problem. However, the problem of sample impoverishment is a constraint of PF. To solve this problem, a cuckoo search-based particle filter is proposed. The particles in PF are optimized using cuckoo search. The meaningful particles are increased and can approximate the true state of the target more accurately. Experiments on visual tracking show that the proposed algorithm outperforms the standard particle filter in solve the visual tracking problems with various challenging conditions.