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RCAA: Relational Context-Aware Agents for Person Search

  • Xiaojun Chang
  • Po-Yao Huang
  • Yi-Dong Shen
  • Xiaodan Liang
  • Yi Yang
  • Alexander G. Hauptmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11213)

Abstract

We aim to search for a target person from a gallery of whole scene images for which the annotations of pedestrian bounding boxes are unavailable. Previous approaches to this problem have relied on a pedestrian proposal net, which may generate redundant proposals and increase the computational burden. In this paper, we address this problem by training relational context-aware agents which learn the actions to localize the target person from the gallery of whole scene images. We incorporate the relational spatial and temporal contexts into the framework. Specifically, we propose to use the target person as the query in the query-dependent relational network. The agent determines the best action to take at each time step by simultaneously considering the local visual information, the relational and temporal contexts, together with the target person. To validate the performance of our approach, we conduct extensive experiments on the large-scale Person Search benchmark dataset and achieve significant improvements over the compared approaches. It is also worth noting that the proposed model even performs better than traditional methods with perfect pedestrian detectors.

Keywords

Person search Relational network 

Notes

Acknowledgements

This work was supported in part by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DOI/IBC) contract number D17PC00340, in part by China National 973 program 2014CB340301, and in part by the Data to Decisions CRC (D2D CRC) and the Cooperative Research Centres Programme. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation/herein. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DOI/IBC, or the U.S. Government.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiaojun Chang
    • 1
  • Po-Yao Huang
    • 1
  • Yi-Dong Shen
    • 2
  • Xiaodan Liang
    • 1
  • Yi Yang
    • 3
  • Alexander G. Hauptmann
    • 1
  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA
  2. 2.Institute of SoftwareChinese Academy of SciencesBeijingChina
  3. 3.Centre for Artificial IntelligenceUniversity of Technology SydneyUltimoAustralia

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