We propose a multi-target tracking algorithm based on the Probability Hypothesis Density (PHD) filter and data association using graph matching. The PHD filter is used to compen...
Emilio Maggio, Elisa Piccardo, Carlo S. Regazzoni,...
Abstract. In recent years, particle filters have emerged as a useful tool that enables the application of Bayesian reasoning to problems requiring dynamic state estimation. The ef...
A distributed set-membership-constrained particle filter (SMCPF) is developed for decentralized tracking applications using wireless sensor networks. Unlike existing PF alternati...
Shahrokh Farahmand, Stergios I. Roumeliotis, Georg...
Particle filter is a powerful algorithm to deal with non-linear and non-Gaussian tracking problems. However the algorithm relying only upon one image cue often fails in challengin...
Abstract. We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or be...