NDLI: Use of contextual information by Bayesian Networks for multi-object tracking in scanning laser range data

Content Provider	IEEE Xplore Digital Library
Author	Lherbier, R. Jida, B. Noyer, J.-C. Wahl, M.
Copyright Year	2009
Description	Author affiliation: Univ. Lille Nord de France, F-59000 Lille, France (Lherbier, R.; Jida, B.; Noyer, J.-C.; Wahl, M.)
Abstract	This paper presents a new method to improve the perception of the environment of a vehicle. The aim here is to detect the vehicles using a scanning laser range finder and track the detected objects at each time. This contribution can be considered as an element of a global vehicle-to-vehicle (v2v) surveillance system where the on-board system receives warnings from the other local systems. It allows to extend the effective surveillance field and as a consequence to provide a faster reaction of the vehicle (collision avoidance or mitigation). To deal with this multi-target tracking problem, we focus on the Joint Probabilistic Data Association (JPDA) methods. Their particularity lies in their ability to take into account the probabilistic characteristics of the detector (detection and false alarm probabilities). Whereas in many works the detection probability is set up once, our contribution is to propose a method that dynamically estimates for each object the detection probability using the contextual information modeled by a Bayesian Network (BN-JPDAF).
Starting Page	97
Ending Page	102
File Size	989497
Page Count	6
File Format	PDF
ISBN	9781424453467
e-ISBN	9781424453474
DOI	10.1109/ITST.2009.5399377
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-20
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multi-object tracking detection joint probabilistic data association Probability Radar tracking Vehicle dynamics Vehicles Laser radar Bayesian methods Vehicle detection Surveillance Radar detection Object detection laser range finder Bayesian networks
Content Type	Text
Resource Type	Article

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