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

Longyin Wen, Dawei Du, Zhaowei Cai, Zhen Lei, Ming Ching Chang, Honggang Qi, Jongwoo Lim, Ming Hsuan Yang, Siwei Lyu

Research output: Contribution to journalArticlepeer-review

250 Citations (Scopus)


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.

Original languageEnglish
Article number102907
JournalComputer Vision and Image Understanding
Publication statusPublished - 2020 Apr

Bibliographical note

Publisher Copyright:
© 2020 Elsevier Inc.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


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