Biogeography based optimization method for robust visual object tracking

Abstract

Moving object tracking is one of the applied fields in artificial intelligence and robotic. The main objective of object tracking is to detect and locate targets in video frames of real scenes. Although various methods have been proposed for object tracking so far, tracking in challenging conditions remains an open issue. Recently, different evolutionary and heuristics algorithms like swarm intelligence have been used to address the tracking challenges, which have shown promising performance. In this paper, a new approach based on modified biogeography based optimization (mBBO) method is introduced. The BBO algorithm includes migration and mutation steps. In the migration phase, the search space is properly explored by sharing information between habitats and weaker solutions to improve their position. On the other hand, the mutation phase leads to diversity and change in solutions. In this algorithm, the elitist method has been also used to keep better solutions. The performance of modified BBO tracker has been evaluated on benchmark video datasets and compared with several other tracking methods. Experimental results demonstrate that the proposed method estimates the location of targets with high accuracy and achieves better performance and robustness compared to other trackers.