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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gidel, S. Checchin, P. Blanc, C. Chateau, T. Trassoudaine, L. |
| Copyright Year | 2008 |
| Description | Author affiliation: LASMEA, Univ. Blaise Pascal, Aubiere (Gidel, S.; Checchin, P.; Blanc, C.; Chateau, T.; Trassoudaine, L.) |
| Abstract | Pedestrian safety is a primary traffic issue in urban environment. This article deals with the detection of pedestrians by means of a laser sensor. This sensor, placed on the front of a vehicle collects information about distance distributed according to 4 laser planes. Like a vehicle, a pedestrian constitutes in the vehicle environment an obstacle which must be detected, located, then identified and tracked if necessary. In order to improve the robustness of pedestrian detection using a single laser sensor we propose here a detection system based on the fusion of information located in the 4 laser planes. In this paper, we propose a Parzen kernel method that allows first to isolate the ldquopedestrian objectsrdquo in each plane and then to carry out a decentralized fusion according to the 4 laser planes. Finally, to improve our pedestrian detection algorithm we use a MCMC based PF method allowing a closer observation of pedestrian random movement dynamics. Many experimental results validate and show the relevance of our pedestrian detection algorithm in regard to a method using only a single-row laser-range scanner. |
| Starting Page | 173 |
| Ending Page | 178 |
| File Size | 680385 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424420575 |
| DOI | 10.1109/IROS.2008.4650700 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-09-22 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robot sensing systems Lasers Vehicles Laser fusion Distance measurement Clustering algorithms Classification algorithms |
| Content Type | Text |
| Resource Type | Article |
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