NDLI: Deep learning for multiple object tracking: a survey

Content Provider	IET Digital Library
Author	Xu, Yingkun Zhou, Xiaolong Chen, Shengyong Li, Fenfen
Abstract	Deep learning has been proved effective in multiple object tracking, which confronts the difficulties of frequent occlusions, confusing appearance, in-and-out objects, and lack of enough labelled data. Recently, deep learning based multi-object tracking methods make a rapid progress from representation learning to network modelling due to the development of deep learning theory and benchmark setup. In this study, the authors summarise and analyse deep learning based multi-object tracking methods which are top-ranked in the public benchmark test. First, they investigate functionality of deep networks in these methods, and classify the methods into three categories as description enhancement using deep features, deep network embedding, and end-to-end deep network construction. Second, they review deep network structures in these methods, and detail the usage and training of these networks for multi-object tracking problem. Through experimental comparison of tracking results in the benchmarks in total and by group, they finally show the effectiveness of deep networks for tracking employed in different manners, and compare the advantages of these networks and their robustness under different tracking conditions. Moreover, they analyse the limitations of current methods, and draw some useful conclusions to facilitate the exploration of new directions for multi-object tracking.
Starting Page	355
Ending Page	368
Page Count	14
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 4, Jun (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5598
Journal	IET Computer Vision
Publisher Date	2019-01-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Deep Learning Theory Deep Network Structures Description Enhancement End-to-end Deep Network Construction Learning in AI Multiobject Tracking Method Multiple Object Tracking Network Modelling Neural Computing Technique Neural Nets Object Detection Object Tracking Optical, Image And Video Signal Processing Public Benchmark Test Representation Learning Tracking Condition
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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