多目标跟踪算法在智能交通监控系统上的研究进展
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作者单位:

1.浙江大学湖州研究院2.湖州师范学院信息工程学院;2.湖州师范学院信息工程学院;3.1.浙江大学湖州研究院

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中图分类号:

TP391

基金项目:

国家自然科学基金项目(项目号:61775139)浙江省科技厅重点研发计划项目(项目号:2020C02020)


Research Progress of Multi-object Tracking Algorithm in Intelligent Traffic Monitoring System
Author:
Affiliation:

1.Huzhou Institute of Zhejiang University2.School of Information Engineering, Huzhou University;2.School of Information Engineering, Huzhou University;3.1.Huzhou Institute of Zhejiang University

Fund Project:

The National Natural Science Foundation of China(No. 61775139); the Zhejiang key R & D plan (No. 2020C02020)

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    摘要:

    为了构建人、路、车、云协同一体化的智能交通监控系统,多目标跟踪的研究具有广泛的应用价值。传统的手工设计特征的方法对高层信息的表征能力有限,较难进行复杂场景下的多目标跟踪。深度学习以其强大的学习能力,逐渐渗透到各个行业和领域,掀起了智能化浪潮。随着深度学习的发展,多目标跟踪算法的性能也取得了较大的进展。为了宏观把握基于深度学习的多目标跟踪算法的研究进展,首先,比较了基于检测的跟踪算法、基于联合检测与跟踪算法、基于单目标跟踪器的多目标跟踪算法的优缺点。其次,介绍了多目标跟踪算法在智能交通监控场景的应用。最后总结了目前多目标跟踪存在的问题与挑战,对多目标跟踪算法未来在智能交通领域的发展进行了思考和展望。

    Abstract:

    To build the integrated Intelligent Traffic Monitoring System based on the cooperation of human, road, vehicle and cloud, the research of multi-object tracking has wide application potentials. Traditional methods with handcrafted features are hard to fully represent high-level information, making it difficult to track multi-targets in complex scenes. Deep learning with its powerful learning ability, has gradually been used in various industries and fields, setting off a wave of smart technologies. To understand the research progress on multi-object tracking algorithm based on deep learning, firstly, the pros and cons of three tracking algorithms, namely Tracking by Detection, Joint Detection and Tracking as well as Multi-Object Tracking with Single Object Tracker, are compared. Secondly, the applications of multi-object tracking algorithm in Intelligent Traffic Monitoring System are introduced. Finally, the problems and challenges of multi-object tracking algorithm are concluded, and the growing trend of multi-object algorithms in Intelligent Transportation field is discussed and forecasted.

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  • 收稿日期:2021-10-13
  • 最后修改日期:2022-02-15
  • 录用日期:2022-02-25
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