Adaptive Interacting Multiple Models applied on pedestrian tracking in car parks

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Abstract
To address perception problems we must be able to track dynamics targets of the environment. An important issue of tracking is filtering problem in which estimates of the target's state are computed while observations are progressively received. This paper presents an adaptive interacting multiple models (IMM) based filtering method. Interacting multiple models have been successfully applied to many applications as they allow, using several filters in parallel, to deal with the uncertainty on motion model, a critical component of filtering. Indeed targets can rapidly change their motion over a lapse of time. This is the case of pedestrians for which it is difficult to define an unique motion model which matches all their possible displacements. Nevertheless, the transition probability matrix (TPM) which models the interaction between different filters in an IMM is in currently defined a priori or needs an important amount of tuning to be used efficiently. In this paper, we put forward a method which automatically adapts online the TPM. The TPM adaptation using on-line data significantly improves the effectiveness of IMM filtering and so better target estimates are obtained. To validate our work we applied our method to pedestrian tracking in car parks on a real platform

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