Tracking of Multiple Soccer Players Using a 3d Particle Filter Based on Detector Confidence

In this paper, a novel method for tracking multiple soccer players based on a support vector machine (SVM) and a particle filter is proposed for a monocular fixed soccer video. This paper considers three main topics. First, the state of a particle filter presented in a 3D world coordinate space, instead of a 2D image coordinate space, provides a more realistic model tracking because depth considerations are taken into account. Second, by integrating SVM into the particle filter framework, the initial positions of the players can be detected or rediscovered when the tracker has lost the players. Third, the feature histogram and correlation are combined when the likelihood for a particle is computed. The effectiveness of this method has been confirmed by experiments that actually tracked multiple soccer players.