Structure preserving clustering-object tracking via subgroup motion pattern segmentation

Zheyi Fan; Yixuan Zhu; Jiao Jiang; Shuqin Weng; Zhiwen Liu

doi:10.1117/1.OE.57.1.013101

12 January 2018 Structure preserving clustering-object tracking via subgroup motion pattern segmentation

Zheyi Fan, Yixuan Zhu, Jiao Jiang, Shuqin Weng, Zhiwen Liu

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), Natural Science Foundation of China

Optical Engineering, Vol. 57, Issue 1, 013101 (January 2018). https://doi.org/10.1117/1.OE.57.1.013101

Abstract

Tracking clustering objects with similar appearances simultaneously in collective scenes is a challenging task in the field of collective motion analysis. Recent work on clustering-object tracking often suffers from poor tracking accuracy and terrible real-time performance due to the neglect or the misjudgment of the motion differences among objects. To address this problem, we propose a subgroup motion pattern segmentation framework based on a multilayer clustering structure and establish spatial constraints only among objects in the same subgroup, which entails having consistent motion direction and close spatial position. In addition, the subgroup segmentation results are updated dynamically because crowd motion patterns are changeable and affected by objects’ destinations and scene structures. The spatial structure information combined with the appearance similarity information is used in the structure preserving object tracking framework to track objects. Extensive experiments conducted on several datasets containing multiple real-world crowd scenes validate the accuracy and the robustness of the presented algorithm for tracking objects in collective scenes.

Citation Download Citation

Zheyi Fan, Yixuan Zhu, Jiao Jiang, Shuqin Weng, and Zhiwen Liu "Structure preserving clustering-object tracking via subgroup motion pattern segmentation," Optical Engineering 57(1), 013101 (12 January 2018). https://doi.org/10.1117/1.OE.57.1.013101

Received: 21 August 2017; Accepted: 11 December 2017; Published: 12 January 2018

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KEYWORDS

Detection and tracking algorithms

Motion models

Image segmentation

Video

Motion detection

Optical engineering

Focus stacking software

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