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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.

Building occupancy grids (OGs) in order to model the surrounding environment of a vehicle implies to fusion occupancy information provided by the different embedded sensors in the same grid. The principal difficulty comes from the fact that each can have a different resolution, but also that the resolution of some sensors varies with the location in the field of view. In this article we present a new exact approach to this issue and we explain why the problem of switching coordinate systems is an instance of the texture mapping problem in computer graphics. Therefore we introduce a calculus architecture to build occupancy grids with a graphical processor unit (GPU). Thus we present computational time results that can allow to compute occupancy grids for 50 sensors at frame rate even for a very fine grid. To validate our method, the results with GPU are compared to results obtained through the exact approach.

In this paper, we detail the hardware and software perception system designed and developed to track pedestrians using a set of offboard cameras. It has been used in the context of vulnerable safety in a car park. This architecture is divided in two parts: a fusion part to fusion the data given by the set of offboard cameras and a tracking part to sequentially estimate the position of each pedestrian present in the environment and to determine the number of pedestrians. Finally, some experimental results are presented.