CLC number: TP212.9
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-04-11
Cited: 2
Clicked: 9060
Yin Tian, Hong-hui Dong, Li-min Jia, Si-yu Li. A vehicle re-identification algorithm based on multi-sensor correlation[J]. Journal of Zhejiang University Science C, 2014, 15(5): 372-382.
@article{title="A vehicle re-identification algorithm based on multi-sensor correlation",
author="Yin Tian, Hong-hui Dong, Li-min Jia, Si-yu Li",
journal="Journal of Zhejiang University Science C",
volume="15",
number="5",
pages="372-382",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300291"
}
%0 Journal Article
%T A vehicle re-identification algorithm based on multi-sensor correlation
%A Yin Tian
%A Hong-hui Dong
%A Li-min Jia
%A Si-yu Li
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 5
%P 372-382
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300291
TY - JOUR
T1 - A vehicle re-identification algorithm based on multi-sensor correlation
A1 - Yin Tian
A1 - Hong-hui Dong
A1 - Li-min Jia
A1 - Si-yu Li
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 5
SP - 372
EP - 382
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300291
Abstract: Magnetic sensors can be applied in vehicle recognition. Most of the existing vehicle recognition algorithms use one sensor node to measure a vehicle’s signature. However, vehicle speed variation and environmental disturbances usually cause errors during such a process. In this paper we propose a method using multiple sensor nodes to accomplish vehicle recognition. Based on the matching result of one vehicle’s signature obtained by different nodes, this method determines vehicle status and corrects signature segmentation. The co-relationship between signatures is also obtained, and the time offset is corrected by such a co-relationship. The corrected signatures are fused via maximum likelihood estimation, so as to obtain more accurate vehicle signatures. Examples show that the proposed algorithm can provide input parameters with higher accuracy. It improves the average accuracy of vehicle recognition from 94.0% to 96.1%, and especially the bus recognition accuracy from 77.6% to 92.8%.
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