Publication Details
Vehicle Re-Identification for Automatic Video Traffic Surveillance
Herout Adam, prof. Ing., Ph.D. (DCGM)
vehicle re-identification, traffic monitoring, automatic traffic surveillance
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.
@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"
}