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Conference papers

Fusion at Detection Level for Frontal Object Perception

R. Omar Chavez-Garcia 1 Trung Dung Vu 2 Olivier Aycard 1
2 E-MOTION - Geometry and Probability for Motion and Action
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes
Abstract : Intelligent vehicle perception involves the correct detection and tracking of moving objects. Taking into account all the possible information at early levels of the perception task can improve the final model of the environment. In this paper, we present an evidential fusion framework to represent and combine evidence from multiple lists of sensor detections. Our fusion framework considers the position, shape and appearance information to represent, associate and combine sensor detections. Although our approach takes place at detection level, we propose a general architecture to include it as a part of a whole perception solution. Several experiments were conducted using real data from a vehicle demonstrator equipped with three main sensors: lidar, radar and camera. The obtained results show improvements regarding the reduction of false detections and mis-classifications of moving objects.
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Submitted on : Thursday, June 19, 2014 - 4:02:29 PM
Last modification on : Wednesday, February 2, 2022 - 3:56:47 PM
Long-term archiving on: : Friday, September 19, 2014 - 11:21:43 AM


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  • HAL Id : hal-01010374, version 1


R. Omar Chavez-Garcia, Trung Dung Vu, Olivier Aycard. Fusion at Detection Level for Frontal Object Perception. Intelligent Vehicles Symposium (IV), 2014 IEEE, Jun 2014, Dearborn, Michigan, United States. pp.8. ⟨hal-01010374⟩



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