Abstract
This paper addresses the diagnosis of interactions between pairs of pedestrians in outdoor scenes, using a generative model for the trajectories. It is assumed that pedestrians’ motions are driven by a set of velocity fields, learned from the video signal. This model is extended to account for the interaction among pedestrians, using attractive/repulsive velocity components. An inference algorithm is provided to estimate the attraction/repulsion velocity from the pedestrian trajectory and characterize pedestrians’ interaction. Since we consider multiple motion models switched according to space-varying probabilities, inference is performed by combining a data association filter with a HMM-like forward algorithm. The proposed algorithm is denoted I-PDAF and is tested with synthetic data and pedestrians trajectories.
This work was supported by FCT in the framework of contract PTDC/EEA-CRO/098550/2008, PEst-OE/EEI/LA0009/2011 and PEst-OE/EEI/LA0021/2011.
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Ribeiro, R.A., Marques, J.S., Lemos, J.M. (2013). A Multiple Velocity Fields Approach to the Detection of Pedestrians Interactions Using HMM and Data Association Filters. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_15
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DOI: https://doi.org/10.1007/978-3-642-41914-0_15
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