Game-Theoretic Multiple Target Tracking

Video-based multiple target tracking (MTT) is a challenging task when similar targets are present in close vicinity. Because their visual observations are mixed and difficult to segment, their motions have to be estimated jointly. Most existing approaches perform this joint motion estimation in a centralized fashion and involve searching a rather high dimensional space, and thus leading to quite complicated joint trackers. This paper brings a new view to MTT from a game-theoretic perspective, bridging the joint motion estimation and the Nash equilibrium of a game. Instead of designing a centralized tracker, MTT is decentralized and a set of individual trackers is used, each of which tries to maximize its visual evidence for explaining its motion as well as generates interferences to others. Modelling this competition behavior, a special game is designed so that the difficult joint motion estimation is achieved at the Nash Equilibrium of this game where no individual tracker has incentives to change its motion estimate. This paper substantializes this novel idea in a solid case study where individual trackers are kernel-based trackers. An efficient best response updating procedure is designed to find the Nash equilibrium. The powerfulness of this game-theoretic MTT is shown by promising results on difficult real videos.