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Salient Points Driven Pedestrian Group Retrieval

  • Xiao-Han Chen
  • Jian-Huang LaiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11901)

Abstract

Groups are the primary constituent units of crowd and the study on groups can help us better understand the collective phenomena in public area. In this paper, collection of stable individuals with some social relationship in public area, called group, is selected as the research object, and a novel task of pedestrian group retrieval is introduced. Different from the individual person matching, groups often show high aggregation due to their inherent characteristics, individuals in the group are more occluded. Therefore, the performance of individual person based detection and matching will be affected. At the same time, group matching also needs to handle difficulties like variations in the shape and ordering of people within the group. We then design a salient points driven framework for pedestrian group retrieval across non-overlapping cameras. The work focuses on the problems of overall appearance characteristics extraction of a deformable pedestrian collection and matching of groups at varying scales. Experiments on Pedestrian-Groups dataset demonstrate the effectiveness of our proposed framework for Pedestrian Group retrieval.

Keywords

Salient points Pedestrian group Group retrieval Group entire descriptor 

Notes

Acknowledgments

This work was supported by National Key Research and Development Program of China (2016YFB1001003), the NSFC (61573387).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouPeople’s Republic of China
  2. 2.Faculty of Mathematics and Computer ScienceGuangdong Ocean UniversityZhanjiangPeople’s Republic of China
  3. 3.School of Information Science and Technology, XinHua CollegeSun Yat-sen UniversityGuangzhouPeople’s Republic of China
  4. 4.Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhouPeople’s Republic of China

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