Multiple Hypothesis Tracking with Merged Bounding Box Measurements Considering Occlusion

Tetsutaro YAMADA
Masato GOCHO
Hiroshi KAMEDA

IEICE TRANSACTIONS on Information and Systems   Vol.E105-D    No.8    pp.1456-1463
Publication Date: 2022/08/01
Publicized: 2022/05/09
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2021EDP7197
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
camera,  bounding box,  target tracking,  multiple hypothesis tracking,  

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A new approach for multi-target tracking in an occlusion environment is presented. In pedestrian tracking using a video camera, pedestrains must be tracked accurately and continuously in the images. However, in a crowded environment, the conventional tracking algorithm has a problem in that tracks do not continue when pedestrians are hidden behind the foreground object. In this study, we propose a robust tracking method for occlusion that introduces a degeneration hypothesis that relaxes the track hypothesis which has one measurement to one track constraint. The proposed method relaxes the hypothesis that one measurement and multiple trajectories are associated based on the endpoints of the bounding box when the predicted trajectory is approaching, therefore the continuation of the tracking is improved using the measurement in the foreground. A numerical evaluation using MOT (Multiple Object Tracking) image data sets is performed to demonstrate the effectiveness of the proposed algorithm.

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