[1]OU Weiqi,YIN Hui,XU Hongli,et al.A multi-object tracking algorithm based on trajectory reconstruction on multi-egocentric video[J].CAAI Transactions on Intelligent Systems,2019,14(2):246-253.[doi:10.11992/tis.201709003]
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A multi-object tracking algorithm based on trajectory reconstruction on multi-egocentric video

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 2 Page number: 246-253 Column: 学术论文—机器学习 Public date: 2019-03-05
Title:
A multi-object tracking algorithm based on trajectory reconstruction on multi-egocentric video
Author(s):
OU Weiqi1; 2;  YIN Hui1; 2;  XU Hongli1; 2;  LIU Zhihao1; 2
1. Department of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
2. Beijing Key Lab of Transportation Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
Keywords:
Multi-Egocentric video; trajectory reconstruction; multi-object tracking; homographic constraint; epipolar geometry constraint; spatial reconstruction; Kalman filter; motion model
CLC:
TP391.4
DOI:
10.11992/tis.201709003
Abstract:
In egocentric video, objects have the characteristics of violent motion, frequent occlusion, so it brings much trouble to carrying out the tracking task. In this paper, we propose a multi-object tracking algorithm based on the motion trajectory reconstruction of multi-egocentric video from different visual angles egocentric videos. First, this method is based on the homographic constraint of multi-view synch frames to fix position of occluded and missing object. Second, using the relative position constraint relation of multi-angle target, the trajectory is reconstructed to locate the target position. Meanwhile, the trajectory of the object is optimized by constructing the motion model of object. Then the continuous trajectory of the object is obtained and the problem of the discontinuity trajectory in multi-object tracking is resolved. In the end, the performance of proposed method has been verified by using the multi-view video dataset which is created by us.
References:
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