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Robust Tracking Occluded Human in Group by Perception Sensors Network System

  • Anh Vu Le
  • JongSuk Choi
Article

Abstract

Tracking people even being partially or fully occluded in the group situation is studied using a Perception Sensor Network (PSN) system which is composed of multiple Kinects used to detect human 3D locations, and pan tilt zoom (PTZ) cameras used to identify human faces. A method is proposed to fuse multiple detection of human in the PSN system. After associating detected human with corresponding names, the novel grouping and ungrouping algorithms are proposed. When a group of multiple human staying close together is formed, viewpoint and illumination invariant features of group members including human 3D location, height, color and binary robust invariant scalable keypoint (BRISK), retrieved from region of interest (ROI) of both depth and color images, are then stored and updated into the group database. Based on the distance between a group location at previous frame and each member location in the group at current frame, the PSN system decides whether to keep the members in the group or to ungroup them then reassign the right name among the group database by minimizing multiple criterions. The experimental results demonstrate the outperforming of the proposed method on tracking people in group than conventional methods.

Keywords

Sensors network Sensor fusion Group tracking Human tracking Features matching 

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Notes

Acknowledgments

The research was partly supported by R&D programs of MOTIE (10041629 [SimonPiC] and 10077468 [DeepTasK]) and by ICT R&D programs of IITP (2015-0-00197 [LISTEN] and 2017-0-00432 [BCI]).

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Optoelectronics Research Group, Faculty of Electrical and Electronics EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Center for Robotics ResearchKorea Institute of Science and TechnologySeoulKorea

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