Abstract
Bat algorithm (BA) is a new meta-heuristic optimization algorithm that is inspired by the echolocation characteristics of bats with varying pulse rates of emission and loudness. BA has been proven to be a powerful tool in solving a wide range of global optimization problems. In this study, visual tracking is considered to be a process of searching for target by various bats in sequential images. A BA-based tracking architecture is proposed and the sensitivity and adjustment of the parameters in BA are studied experimentally. To demonstrate the tracking ability of the proposed tracker, comparative studies of tracking accuracy and speed of the BA-based tracker with three representative trackers, namely, particle filter, meanshift and particle swarm optimization are presented. Comparative results show that the BA-based tracker outperforms the other three trackers.
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