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

doi:10.1016/j.cviu.2020.102907

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking
	Wen, Longyin 1; Du, Dawei 2; Cai, Zhaowei 3; Lei, Zhen4 ; Chang, Ming-Ching 2; Qi, Honggang 5; Lim, Jongwoo 6; Yang, Ming-Hsuan 7; Lyu, Siwei 2
发表期刊	COMPUTER VISION AND IMAGE UNDERSTANDING
ISSN	1077-3142
	2020-04-01
卷号	193 页码:20
通讯作者	Lyu, Siwei(slyu@albany.edu)
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.
关键词	Object detection Object tracking Benchmark Evaluation protocol
DOI	10.1016/j.cviu.2020.102907
关键词[WOS]	MULTITARGET TRACKING ; ROBUST ; APPEARANCE ; HISTOGRAMS
收录类别	SCI
语种	英语
资助项目	US Natural Science Foundation[IIS1816227] ; National Nature Science Foundation of China[61472388] ; National Nature Science Foundation of China[61771341]
项目资助者	US Natural Science Foundation ; National Nature Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000518876100004
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
引用统计	被引频次：242[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/38604
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	Lyu, Siwei
作者单位	1.JD Finance Amer Corp, Mountain View, CA USA 2.SUNY Albany, Dept Comp Sci, Albany, NY 12222 USA 3.Univ Calif San Diego, Dept Elect & Comp Engn, San Diego, CA 92103 USA 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China 6.Hanyang Univ, Div Comp Sci & Engn, Seoul, South Korea 7.Univ Calif Merced, Sch Engn, Merced, CA 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.