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UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking

文献类型：期刊论文

作者Wen, Longyin2; Du, Dawei1; Cai, Zhaowei7; Lei, Zhen6; Chang, Ming-Ching1; Qi, Honggang5; Lim, Jongwoo4; Yang, Ming-Hsuan3; Lyu, Siwei1
刊名COMPUTER VISION AND IMAGE UNDERSTANDING
出版日期2020-04-01
卷号193页码:20
关键词Object detection Object tracking Benchmark Evaluation protocol
ISSN号1077-3142
DOI10.1016/j.cviu.2020.102907
通讯作者Lyu, Siwei(slyu@albany.edu)
英文摘要Effective multi-object tracking (MOT) methods have been developed in recent years for a wide range of applications including visual surveillance and behavior understanding. Existing performance evaluations of MOT methods usually separate the tracking step from the detection step by using one single predefined setting of object detection for comparisons. In this work, we propose a new University at Albany DEtection and TRACking (UA-DETRAC) dataset for comprehensive performance evaluation of MOT systems especially on detectors. The UA-DETRAC benchmark dataset consists of 100 challenging videos captured from real-world traffic scenes (over 140,000 frames with rich annotations, including illumination, vehicle type, occlusion, truncation ratio, and vehicle bounding boxes) for multi-object detection and tracking. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and tracking methods. Our analysis shows the complex effects of detection accuracy on MOT system performance. Based on these observations, we propose effective and informative evaluation metrics for MOT systems that consider the effect of object detection for comprehensive performance analysis.
WOS关键词MULTITARGET TRACKING ; ROBUST ; APPEARANCE ; HISTOGRAMS
资助项目US Natural Science Foundation[IIS1816227] ; National Nature Science Foundation of China[61472388] ; National Nature Science Foundation of China[61771341]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000518876100004
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
资助机构US Natural Science Foundation ; National Nature Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/38603]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者Lyu, Siwei
作者单位1.SUNY Albany, Dept Comp Sci, Albany, NY 12222 USA
2.JD Finance Amer Corp, Mountain View, CA USA
3.Univ Calif Merced, Sch Engn, Merced, CA USA
4.Hanyang Univ, Div Comp Sci & Engn, Seoul, South Korea
5.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
7.Univ Calif San Diego, Dept Elect & Comp Engn, San Diego, CA 92103 USA
推荐引用方式
GB/T 7714		 Wen, Longyin,Du, Dawei,Cai, Zhaowei,et al. UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2020,193:20.
APA Wen, Longyin.,Du, Dawei.,Cai, Zhaowei.,Lei, Zhen.,Chang, Ming-Ching.,...&Lyu, Siwei.(2020).UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking.COMPUTER VISION AND IMAGE UNDERSTANDING,193,20.
MLA Wen, Longyin,et al."UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking".COMPUTER VISION AND IMAGE UNDERSTANDING 193(2020):20.

入库方式： OAI收割

来源：自动化研究所

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