Summary
Different solutions have been proposed for multiple objects tracking based on probabilistic algorithms. In this chapter, the authors propose the use of a single particle filter to track a variable number of objects in a complex environment. Estimator robustness and adaptability are both 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. As a proof of concept, real results are obtained in a long sequence with a mobile robot moving in a cluttered scene.
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Marrón, M., García, J.C., Sotelo, M.A., Pizarro, D., Bravo, I., Martín, J.L. (2008). A Bayesian Solution to Robustly Track Multiple Objects from Visual Data. In: Chountas, P., Petrounias, I., Kacprzyk, J. (eds) Intelligent Techniques and Tools for Novel System Architectures. Studies in Computational Intelligence, vol 109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77623-9_30
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