Vehicle re-identification (re-ID) is based on identity matching of vehicles across non-overlapping camera views. Recently, the research on vehicle re-ID attracts increased attention, mainly due to its prominent industrial applications, such as post-crime analysis, traffic flow analysis, and wide-area vehicle tracking. However, despite the increased interest, the problem remains to be challenging. One of the most significant difficulties of vehicle re-ID is the large viewpoint variations due to non-standardized camera placements. In this study, to improve re-ID robustness against viewpoint variations while preserving algorithm efficiency, we exploit the use of vehicle orientation information. First, we analyze and benchmark various deep learning architectures in terms of performance, memory use, and cost on applicability to orientation classification. Secondly, the extracted orientation information is utilized to improve the vehicle re-ID task. For this, we propose a viewpoint-aware multi-branch network that improves the vehicle re-ID performance without increasing the forward inference time. Third, we introduce a viewpoint-aware mini-batching approach which yields improved training and higher re-ID performance. The experiments show an increase of 4.0% mAP and 4.4% rank-1 score on the popular VeRi dataset with the proposed mini-batching strategy, and overall, an increase of 2.2% mAP and 3.8% rank-1 score compared to the ResNet-50 baseline.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Real-time vehicle orientation classification and viewpoint-aware vehicle re-identification


    Beteiligte:

    Erscheinungsdatum :

    2021-01-01


    Anmerkungen:

    Kocsis , O T , Alkanat , T , Bondarev , E & De With , P H N 2021 , ' Real-time vehicle orientation classification and viewpoint-aware vehicle re-identification ' , IS and T International Symposium on Electronic Imaging Science and Technology , vol. 2021 , no. 10 , 234 . https://doi.org/10.2352/ISSN.2470-1173.2021.10.IPAS-234



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Viewpoint-Aware Progressive Clustering for Unsupervised Vehicle Re-Identification

    Zheng, Aihua / Sun, Xia / Li, Chenglong et al. | IEEE | 2022


    VARID: Viewpoint-Aware Re-IDentification of Vehicle Based on Triplet Loss

    Li, Yidong / Liu, Kai / Jin, Yi et al. | IEEE | 2022


    Orientation-Aware Vehicle Re-Identification with Semantics-Guided Part Attention Network

    Chen, Tsai-Shien / Liu, Chih-Ting / Wu, Chih-Wei et al. | British Library Conference Proceedings | 2020


    Real-Time Vehicle Tracking and Classification

    Noll, D. / Werner, M. / Von Seelen, W. et al. | British Library Conference Proceedings | 1995