A Bayesian Solution to Track Multiple and Dynamic Objects Robustly from Visual Data
Marta Marrón-Romera, J.C. García, Miguel Ángel Sotelo, José Luis Marcos Martín · 2006 3rd International IEEE Conference Intelligent Systems · 2006
Different solutions have been proposed for multiple objects tracking based on probabilistic algorithms. In this paper, the authors propose the use of an only particle filter to track a variable number of objects. The estimator robustness and adaptability are increased by the use of a clustering algorithm. Measurements used in the tracking process are extracted from a stereovision system, and thus, the 3D position of the tracked objects is obtained at each time step. Tracking results are presented at the end of the paper