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Vehicle re-identification using multi-task deep learning network and spatio-temporal model

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Abstract

Vehicle re-identification (re-ID) plays an important role in the automatic analysis of the increasing urban surveillance videos and has become a hot topic in recent years. Vehicle re-ID aims at identifying vehicles across different cameras. However, it suffers from the difficulties caused by various viewpoint of vehicles, diversified illuminations, and complicated environments. In this paper, a two-stage vehicle re-ID framework is proposed to address these challenges, which contains a feature extraction module for achieving discriminative features and a spatial-temporal re-ranking module to improve the accuracy of vehicle re-ID task. Firstly, a multi-task deep network that integrates identity predicting network, attribute recognition network and verification network is adopted to learn discriminate features. Secondly, a spatio-temporal model is built to re-rank the appearance information measurement results, which utilizes the spatio-temporal relationship to increase constraints of the images. Moreover, to facilitate progressive vehicle re-ID research, experiments are conducted on both the VeRi-776 dataset and VehicleID dataset. Both the proposed multi-task feature extraction module and spatio-temporal model achieve considerable improvements.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China Grant 61370142 and Grant 61272368, by the Fundamental Research Funds for the Central Universities Grant 3132016352, by the Fundamental Research of Ministry of Transport of P. R. China Grant 2015329225300, by the Dalian Science and Technology Innovation Fund 2019J11CY001 and Dalian Leading talent Grant, by the Foundation of Liaoning Key Research and Development Program, China Postdoctoral Science Foundation 3620080307, by Liaoning Revitalization Talents Program, XLYC1908007, by Dalian Science and Technology Innovation Fund 2019J11CY001, by the Fundamental Research Funds for the Central Universities Grant 3132016352.

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Authors and Affiliations

  1. College of Information and Science Technology, Dalian Maritime University, Dalian, Liaoning, 116021, China

    Jinjia Peng, Yun Hao, Fengqiang Xu & Xianping Fu

  2. Pengcheng Laboratory, Shenzhen, Guangdong, 518055, China

    Xianping Fu

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  1. Jinjia Peng
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  2. Yun Hao
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Correspondence to Xianping Fu.

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Peng, J., Hao, Y., Xu, F. et al. Vehicle re-identification using multi-task deep learning network and spatio-temporal model. Multimed Tools Appl 79, 32731–32747 (2020). https://doi.org/10.1007/s11042-020-09356-w

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