Groupwise Tracking of Crowded Similar-Appearance Targets From Low-Continuity Image Sequences

Hongkai Yu, Youjie Zhou, Jeff Simmons, Craig P. Przybyla, Yuewei Lin, Xiaochuan Fan, Yang Mi, Song Wang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 952-960

Abstract


Automatic tracking of large-scale crowded targets are of particular importance in many applications, such as crowded people/vehicle tracking in video surveillance, fiber tracking in materials science, and cell tracking in biomedical imaging. This problem becomes very challenging when the targets show similar appearance and the inter-slice/inter-frame continuity is low due to sparse sampling, camera motion and target occlusion. The main challenge comes from the step of association which aims at matching the predictions and the observations of the multiple targets. In this paper we propose a new groupwise method to explore the target group information and employ the within-group correlations for association and tracking. In particular, the within-group association is modeled by a nonrigid 2D Thin-Plate transform and a sequence of group shrinking, group growing and group merging operations are then developed to refine the composition of each group. We apply the propose method to track large-scale fibers from the microscopy material images and compare its performance against several other multi-target tracking methods. We also apply the proposed method to track crowded people from videos with poor inter-frame continuity.

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[bibtex]
@InProceedings{Yu_2016_CVPR,
author = {Yu, Hongkai and Zhou, Youjie and Simmons, Jeff and Przybyla, Craig P. and Lin, Yuewei and Fan, Xiaochuan and Mi, Yang and Wang, Song},
title = {Groupwise Tracking of Crowded Similar-Appearance Targets From Low-Continuity Image Sequences},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}