{"title":"A Review of Multi-Object Tracking in Recent Times","authors":"Suya Li, Hengyi Ren, Xin Xie, Ying Cao","doi":"10.1049/cvi2.70010","DOIUrl":null,"url":null,"abstract":"<p>Multi-object tracking (MOT) is a fundamental problem in computer vision that involves tracing the trajectories of foreground targets throughout a video sequence while establishing correspondences for identical objects across frames. With the advancement of deep learning techniques, methods based on deep learning have significantly improved accuracy and efficiency in MOT. This paper reviews several recent deep learning-based MOT methods and categorises them into three main groups: detection-based, single-object tracking (SOT)-based, and segmentation-based methods, according to their core technologies. Additionally, this paper discusses the metrics and datasets used for evaluating MOT performance, the challenges faced in the field, and future directions for research.</p>","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"19 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.70010","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Computer Vision","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cvi2.70010","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Multi-object tracking (MOT) is a fundamental problem in computer vision that involves tracing the trajectories of foreground targets throughout a video sequence while establishing correspondences for identical objects across frames. With the advancement of deep learning techniques, methods based on deep learning have significantly improved accuracy and efficiency in MOT. This paper reviews several recent deep learning-based MOT methods and categorises them into three main groups: detection-based, single-object tracking (SOT)-based, and segmentation-based methods, according to their core technologies. Additionally, this paper discusses the metrics and datasets used for evaluating MOT performance, the challenges faced in the field, and future directions for research.
期刊介绍：
IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in computer vision.
IET Computer Vision welcomes submissions on the following topics:
Biologically and perceptually motivated approaches to low level vision (feature detection, etc.);
Perceptual grouping and organisation
Representation, analysis and matching of 2D and 3D shape
Shape-from-X
Object recognition
Image understanding
Learning with visual inputs
Motion analysis and object tracking
Multiview scene analysis
Cognitive approaches in low, mid and high level vision
Control in visual systems
Colour, reflectance and light
Statistical and probabilistic models
Face and gesture
Surveillance
Biometrics and security
Robotics
Vehicle guidance
Automatic model aquisition
Medical image analysis and understanding
Aerial scene analysis and remote sensing
Deep learning models in computer vision
Both methodological and applications orientated papers are welcome.
Manuscripts submitted are expected to include a detailed and analytical review of the literature and state-of-the-art exposition of the original proposed research and its methodology, its thorough experimental evaluation, and last but not least, comparative evaluation against relevant and state-of-the-art methods. Submissions not abiding by these minimum requirements may be returned to authors without being sent to review.
Special Issues Current Call for Papers:
Computer Vision for Smart Cameras and Camera Networks - https://digital-library.theiet.org/files/IET_CVI_SC.pdf
Computer Vision for the Creative Industries - https://digital-library.theiet.org/files/IET_CVI_CVCI.pdf
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