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Convoy Detection in Crowded Surveillance Videos

  • Zeyd BoukhersEmail author
  • Yicong Wang
  • Kimiaki Shirahama
  • Kuniaki Uehara
  • Marcin Grzegorzek
Conference paper
  • 517 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9997)

Abstract

This paper proposes detection of convoys in a crowded surveillance video. A convoy is defined as a group of pedestrians who are moving or standing together for a certain period of time. To detect such convoys, we firstly address pedestrian detection in a crowded scene, where small regions of pedestrians and their strong occlusions render usual object detection methods ineffective. Thus, we develop a method that detects pedestrian regions by clustering feature points based on their spatial characteristics. Then, positional transitions of pedestrian regions are analysed by our convoy detection method that consists of the clustering and intersection processes. The former finds groups of pedestrians in one frame by flexibly handling their relative spatial positions, and the latter refines groups into convoys by considering their temporal consistences over multiple frames. The experimental results on a challenging dataset shows the effectiveness of our convoy detection method.

Keywords

Crowded video surveillance Group activities Convoy detection Pedestrian detection in crowded scenes 

Notes

Acknowledgments

The research work by Zeyd Boukhers leading to this article has been funded by the German Academic Exchange Service (DAAD). Research and development activities in this article have been in part supported by the German Federal Ministry of Education and Research within the project “Cognitive Village: Adaptively Learning Technical Support System for Elderly” (Grant Number: 16SV7223K).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Zeyd Boukhers
    • 1
    Email author
  • Yicong Wang
    • 2
  • Kimiaki Shirahama
    • 1
  • Kuniaki Uehara
    • 2
  • Marcin Grzegorzek
    • 1
  1. 1.Research Group for Pattern RecognitionUniversity of SiegenSiegenGermany
  2. 2.Graduate School of System InformaticsKobe UniversityKobeJapan

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