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ZAPLETAL, D. HEROUT, A.
Original Title
Vehicle Re-Identification for Automatic Video Traffic Surveillance
Type
conference paper
Language
English
Original Abstract
This paper proposes an approach to the vehicle re-identification problem in a multiple camera system. We focused on the re-identification itself assuming that the vehicle detection problem is already solved including extraction of a full-fledged 3D bounding box. The re-identification problem is solved by using color histograms and histograms of oriented gradients by a linear regressor. The features are used in separate models in order to get the best results in the shortest CPU computation time. The proposed method works with a high accuracy (60% true positives retrieved with 10% false positive rate on a challenging subset of the test data) in 85 milliseconds of the CPU (Core i7) computation time per one vehicle re-identification assuming the fullHD resolution video input. The applications of this work include finding important parameters such as travel time, traffic flow, or traffic information in a distributed traffic surveillance and monitoring system.
Keywords
vehicle re-identification, traffic monitoring, automatic traffic surveillance
Authors
ZAPLETAL, D.; HEROUT, A.
Released
30. 6. 2016
Publisher
IEEE Computer Society
Location
Las Vegas
ISBN
978-0-7695-4989-7
Book
International Workshop on Automatic Traffic Surveillance (CVPR 2016)
Pages from
1568
Pages to
1574
Pages count
7
BibTex
@inproceedings{BUT130978, author="Dominik {Zapletal} and Adam {Herout}", title="Vehicle Re-Identification for Automatic Video Traffic Surveillance", booktitle="International Workshop on Automatic Traffic Surveillance (CVPR 2016)", year="2016", pages="1568--1574", publisher="IEEE Computer Society", address="Las Vegas", doi="10.1109/CVPRW.2016.195", isbn="978-0-7695-4989-7" }