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Poses Guide Spatiotemporal Model for Vehicle Re-identification

  • Xian Zhong
  • Meng Feng
  • Wenxin Huang
  • Zheng Wang
  • Shin’ichi Satoh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)

Abstract

In this paper, we tackle the vehicle Re-identification (Re-ID) problem, which is important in the urban surveillance. Utilizing visual appearance information is limited on performance due to occlusions, illumination variations, etc. To make the best of our knowledge, the recent few methods consider the spatiotemporal information to solve vehicle Re-ID problem, and neglect the influence of driving direction. In this paper, we explore that the spatiotemporal distribution of vehicle movements follows certain rules, moreover the vehicles’ poses on camera view indicate their directions are closely related to the spatiotemporal cues. Inspired by these two observations, we propose a vehicles’ Poses Guide Spatiotemporal model (PGST) for assisting vehicle Re-ID. Firstly, a Gaussian distribution based spatiotemporal probability model is exploited to predict the vehicle’s spatiotemporal movement. Then a CNN embedding poses classifier is exploited to estimate driving direction by evaluating vehicle’s pose. Finally, PGST model is integrated into the framework which fuses the results of visual appearance model and spatiotemporal model together. Due to the lack of vehicle dataset with spatiotemporal information and topology of cameras, experiments are conducted on a public vehicle Re-ID dataset which is the only one meeting the experiments requirements. The proposed approach achieves competitive performances.

Keywords

Vehicle re-identification Spatiotemporal model Vehicle poses classifier 

Notes

Acknowledgement

The research was supported by National Nature Science Foundation of China (61572012, 61801335), Hubei Provincial Natural Science Foundation of China (2015CFB52, 2017CFA012).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xian Zhong
    • 1
  • Meng Feng
    • 1
  • Wenxin Huang
    • 1
    • 2
  • Zheng Wang
    • 3
  • Shin’ichi Satoh
    • 3
  1. 1.Wuhan University of TechnologyWuhanChina
  2. 2.Wuhan UniversityWuhanChina
  3. 3.National Institute of InformaticsTokyoJapan

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