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Multi person detection and tracking based on hierarchical level-set method
Author(s): Chadia Khraief; Faouzi Benzarti; Hamid Amiri
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Paper Abstract

In this paper, we propose an efficient unsupervised method for mutli-person tracking based on hierarchical level-set approach. The proposed method uses both edge and region information in order to effectively detect objects. The persons are tracked on each frame of the sequence by minimizing an energy functional that combines color, texture and shape information. These features are enrolled in covariance matrix as region descriptor. The present method is fully automated without the need to manually specify the initial contour of Level-set. It is based on combined person detection and background subtraction methods. The edge-based is employed to maintain a stable evolution, guide the segmentation towards apparent boundaries and inhibit regions fusion. The computational cost of level-set is reduced by using narrow band technique. Many experimental results are performed on challenging video sequences and show the effectiveness of the proposed method.

Paper Details

Date Published: 13 April 2018
PDF: 7 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960G (13 April 2018); doi: 10.1117/12.2310149
Show Author Affiliations
Chadia Khraief, Univ. of Tunis El Manar (Tunisia)
National Engineering School of Tunis (Tunisia)
Signal, Image and Technology Information (Tunisia)
Faouzi Benzarti, Univ. of Tunis El Manar (Tunisia)
National Engineering School of Tunis (Tunisia)
Signal, Image and Technology Information (Tunisia)
Hamid Amiri, Univ. of Tunis El Manar (Tunisia)
National Engineering School of Tunis (Tunisia)
Signal, Image and Technology Information (Tunisia)


Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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