A Multiagent-based Video Tracking Algorithm

Abstract

One well known and long-lasting problem in the video tracking is that one particular algorithm would perform well on a certain environmental characteristic. Whenever the characteristic in the scene changes, the performance of the algorithm affected. This research proposes a multiagent-based for video tracking system. The agents follow the odd-man out strategy, which odd agents will be credited less than the favorite ones. We tested our algorithm against two tough videos. The results show that our approach yield satisfactory outcomes. The final tracking results are always within the boundary of the groundtruth, given that there are two out of five correct results.