Abstract
In this paper we propose an efficient and robust real-time tracking algorithm, able to deal with the common errors occurring in the object detection systems, like total or partial occlusions. Most of the common tracking algorithms make their tracking decisions by comparing the evidence at the current frame with the objects known at the previous one; the main novelty of our method lies in the fact that it takes into account also the history of each object. To exploit this idea, the algorithm adopts an object model based on a set of scenarios, implemented by a Finite State Automaton (FSA), in order to differently deal with objects depending on their recent history. An experimental evaluation of the algorithm has been performed using the PETS2010 database, comparing the obtained performance with the results of the PETS2010 contest participants.
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Di Lascio, R., Foggia, P., Saggese, A., Vento, M. (2013). A Robust People Tracking Algorithm Using Contextual Reasoning for Recovering Detection Errors. In: Csurka, G., Kraus, M., Laramee, R.S., Richard, P., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics. Theory and Application. Communications in Computer and Information Science, vol 359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38241-3_15
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DOI: https://doi.org/10.1007/978-3-642-38241-3_15
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