Multiple object tracking: A literature review

计算机科学 数据科学 领域(数学) 分类 钥匙(锁) 对象(语法) 管理科学 人工智能 风险分析(工程) 工程类 计算机安全 数学 医学 纯数学
作者
Wenhan Luo,Junliang Xing,Anton Milan,Xiaoqin Zhang,Wei Liu,Tae‐Kyun Kim
出处
期刊:Artificial Intelligence [Elsevier]
卷期号:293: 103448-103448 被引量:533
标识
DOI:10.1016/j.artint.2020.103448
摘要

Multiple Object Tracking (MOT) has gained increasing attention due to its academic and commercial potential. Although different approaches have been proposed to tackle this problem, it still remains challenging due to factors like abrupt appearance changes and severe object occlusions. In this work, we contribute the first comprehensive and most recent review on this problem. We inspect the recent advances in various aspects and propose some interesting directions for future research. To the best of our knowledge, there has not been any extensive review on this topic in the community. We endeavor to provide a thorough review on the development of this problem in recent decades. The main contributions of this review are fourfold: 1) Key aspects in an MOT system, including formulation, categorization, key principles, evaluation of MOT are discussed; 2) Instead of enumerating individual works, we discuss existing approaches according to various aspects, in each of which methods are divided into different groups and each group is discussed in detail for the principles, advances and drawbacks; 3) We examine experiments of existing publications and summarize results on popular datasets to provide quantitative and comprehensive comparisons. By analyzing the results from different perspectives, we have verified some basic agreements in the field; and 4) We provide a discussion about issues of MOT research, as well as some interesting directions which will become potential research effort in the future.

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