Publication Details
Vehicle Re-Identification and Multi-Camera Tracking in Challenging City-Scale Environment
Bartl Vojtěch, Ing., Ph.D. (DCGM)
Juránek Roman, Ing., Ph.D. (DCGM)
Herout Adam, prof. Ing., Ph.D. (DCGM)
vehicle re-identification, vehicle multi-camera tracking, city-scale environment, camera calibration, neural networks, nvidia ai city challenge
In our submission to the NVIDIA AI City Challenge, we address vehicle re-identification and vehicle multi-camera tracking. Our approach to vehicle re-identification is based on the extraction of visual features and aggregation of these features in the temporal domain to obtain a single feature descriptor for the whole observed track. For multi-camera tracking, we proposed a method for matching vehicles by the position of trajectory points in real-world space (linear coordinate system). Furthermore, we use CNN for the vehicle re-identification task to filter out false matches generated by proposed positional matching method for better results.
@inproceedings{BUT162081,
author="Jakub {Špaňhel} and Vojtěch {Bartl} and Roman {Juránek} and Adam {Herout}",
title="Vehicle Re-Identification and Multi-Camera Tracking in Challenging City-Scale Environment",
booktitle="2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
year="2019",
series="IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
volume="2019",
number="1",
pages="150--158",
publisher="IEEE Computer Society",
address="Long Beach",
issn="2160-7516",
url="http://openaccess.thecvf.com/content_CVPRW_2019/html/AI_City/Spanhel_Vehicle_Re-Identifiation_and_Multi-Camera_Tracking_in_Challenging_City-Scale_Environment_CVPRW_2019_paper.html"
}