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Neural Computing and Applications

, Volume 28, Supplement 1, pp 127–141 | Cite as

Online adaptive multiple pedestrian tracking in monocular surveillance video

  • Zhihui Wang
  • Sook YoonEmail author
  • Dong Sun Park
Original Article
  • 409 Downloads

Abstract

Automatic online multiple pedestrian tracking is a rather important and challenging task in the field of machine vision. A new multiple pedestrian tracking system is proposed in this paper, which combines pedestrian detection, motion prediction, target matching and adaptive location adjustment methods. The clip-split strategy was adopted for optimization of the detected pedestrian candidates, which resulted in great improvement of the tracking accuracies, especially when the marginal areas of the detected target candidates contained background scenes. For each frame, the proposed adaptive location adjustment method was used to adjust the location and scale of the targets to deal with drifting problems where necessary, especially after severe occlusions. Experimental results on three challenging real-world datasets demonstrated that the proposed tracker has excellent performance over other state-of-the-art trackers based on MOT metrics.

Keywords

Visual tracking Pedestrian detection Clip-split strategy Adaptive location adjustment MOT metrics 

Notes

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2013778) and also supported (in part) by Research Funds of Mokpo National University in 2013.

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.Department of Electronics EngineeringChonbuk National UniversityJeonjuSouth Korea
  2. 2.Department of Multimedia EngineeringMokpo National UniversityJeonnamSouth Korea
  3. 3.IT Convergence Research CenterChonbuk National UniversityJeonjuSouth Korea
  4. 4.Division of Electronics EngineeringChonbuk National UniversityJeonjuSouth Korea

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