Abstract
In this document two methods for a multiple object tracking problem are tested and compared in a 2D environment with quantisied vision considering the tracking problem as a constraint satisfaction problem as a general approach. The first method is a qualifier method which uses three probabilistic models (identity, distance, and movement direction) to compute the belief of the path of a given object considering the path a Markov process. The second method are particle filters with penalised predictions which expands the belief of a given objects in order to get the best match for it. Each method was tested in two situations. In the first situation the observer was static in a fixed position while the second situation involves a dynamic observer. The methods obtained an almost perfect result of 98 % of correct matches for the first situation and achieved a result of nearly 78 % of correct matches in the second situation.
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González, N.I., Garrido, L. (2014). Comparison of a New Qualifier Method for Multiple Object Tracking in RoboCup 2D Simulation League. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_58
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DOI: https://doi.org/10.1007/978-3-319-12027-0_58
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