Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking

Abstract : —The accurate detection and classification of moving objects is a critical aspect of Advanced Driver Assistance Systems (ADAS). We believe that by including the objects classification from multiple sensors detections as a key component of the object's representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to include class information in the core object's description. Second , we propose a complete perception fusion architecture based on the Evidential framework to solve the Detection and Tracking of Moving Objects (DATMO) problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the interactIVe IP European project which includes three main sensors: radar, lidar and camera. We test our fusion approach using real data from different driving scenarios and focusing on four objects of interest: pedestrian, bike, car and truck.
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IEEE Transactions on Intelligent Transportation Systems, IEEE, 2015, PP (99), pp.1-10. 〈10.1109/TITS.2015.2479925〉
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Contributeur : Olivier Aycard <>
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R Omar Chavez-Garcia, Olivier Aycard. Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking. IEEE Transactions on Intelligent Transportation Systems, IEEE, 2015, PP (99), pp.1-10. 〈10.1109/TITS.2015.2479925〉. 〈hal-01241846〉

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