arrow
Return

Multiobject Tracking by Submodular Optimization

delete2019-06-01
delete69
PRE
AI
沈建冰 (Jianbing Shen) *
Z
Zhiyuan Liang
J
Jian‐Hong Liu
H
Hanqiu Sun
L
Ling Shao
D
Dacheng Tao
DOI:10.1109/TCYB.2018.2803217delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
In this paper, we propose a new multiobject visual tracking algorithm by submodular optimization. The proposed algorithm is composed of two main stages. At the first stage, a new selecting strategy of tracklets is proposed to cope with occlusion problem. We generate low-level tracklets using overlap criteria and man-cost How, respectively, and then integrate them into a candidate tracklets set. In the second stage, we formulate the multiobject tracking problem as the submodular maximization problem subject to related constraints. The submodular function selects the correct tracklets from the candidate set of tracklets to form the object trajectory. Then, we design a connecting process which connects the corresponding trajectories to overcome the occlusion problem. Experimental results demonstrate the effectiveness of our tracking algorithm.
Keywords:
Low-level tracklet
multiobject tracking (MOT)
submodular optimization
tracklets selecting process
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Transactions on Cybernetics cover
IEEE Transactions on Cybernetics
IF:
10.5
Papers:
1.1W
Citations:
5.0W

Organization

B
beijing institute of technology
Scholars:
5.4W
Papers: 3.9W
Citations: 63
U
University of East Anglia
Scholars:
9.6K
Papers: 1.0W
Citations: 1.8W
C
Chinese University of Hong Kong
Scholars:
3.4W
Papers: 3.2W
Citations: 5.6W
U
university of technology sydney
Scholars:
1.6W
Papers: 2.0W
Citations: 25
researcher View more organizations