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An Occlusion-Aware Multi-Target Multi-Camera Tracking System

 
: Specker, Andreas; Stadler, Daniel; Florin, Lucas; Beyerer, Jürgen

:
Volltext urn:nbn:de:0011-n-6401798 (1.1 MByte PDF)
MD5 Fingerprint: 7275543ef7832b842799ff79b0828c80
Erstellt am: 4.9.2021


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society; Computer Vision Foundation -CVF-:
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021. Proceedings : Virtual, 19 - 25 June 2021
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2021
ISBN: 978-1-6654-4899-4
ISBN: 978-1-6654-4900-7
S.4168-4177
Conference on Computer Vision and Pattern Recognition (CVPR) <2021, Online>
AI City Challenge Workshop (AICity) <2021, Online>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
Multi-camera tracking of vehicles on a city-scale level is a crucial task for efficient traffic monitoring. Most of the errors made by such multi-target multi-camera tracking systems arise due to tracking failures or misleading visual information of detection boxes under occlusion. Therefore, we propose an occlusion-aware approach that leverages temporal information from tracks to improve the single-camera tracking performance by an occlusion handling strategy and additional modules to filter false detections. For the multi-camera tracking, we discard obstacle-occluded detection boxes by a background filtering technique and boxes overlapping with other targets using the available track information to improve the quality of extracted visual features. Furthermore, topological and temporal constraints are incorporated to simplify the re-identification task in the multi-camera clustering. We give detailed insights into our method with ablative experiments and show its competitiveness on the CityFlowV2 dataset, where we achieve promising results ranking 4th in Track 3 of the 2021 AI City Challenge.

: http://publica.fraunhofer.de/dokumente/N-640179.html