A Boosted Particle Filter: Multitarget Detection and Tracking

The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and target distributions can be highly non-linear and non-Gaussian. Second, the presence of a large, varying number of objects creates complex interactions with overlap and ambiguities. To surmount these difficulties, we introduce a vision system that is capable of learning, detecting and tracking the objects of interest. The system is demonstrated in the context of tracking hockey players using video sequences. Our approach combines the strengths of two successful algorithms: mixture particle filters and Adaboost. The mixture particle filter [17] is ideally suited to multi-target tracking as it assigns a mixture component to each player. The crucial design issues in mixture particle filters are the choice of the proposal distribution and the treatment of objects leaving and entering the scene. Here, we construct the proposal distribution using a mixture model that in...

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Kenji Okuma, Ali Taleghani, Nando de Freitas, James J. Little, David G. Lowe
• Keywords: Adaboost Proposal Distribution, Computer Vision, ECCV 2004, Mixture Component, Mixture Model, Mixture Particle Filters, Multiple Object Tracking