The ParallelEye Dataset: A Large Collection of Virtual Images for Traffic Vision Research

Dataset plays an essential role in the training and testing of traffic vision algorithms. However, the collection and annotation of images from the real world is time-consuming, labor-intensive, and error-prone. Therefore, more and more researchers have begun to explore the virtual dataset, to overcome the disadvantages of real datasets. In this paper, the authors propose a systematic method to construct large-scale artificial scenes and collect a new virtual dataset (named “ParallelEye”) for the traffic vision research. The Unity3D rendering software is used to simulate environmental changes in the artificial scenes and generate ground-truth labels automatically, including semantic/instance segmentation, object bounding boxes, and so on. In addition, they utilize ParallelEye in combination with real datasets to conduct experiments. The experimental results show the inclusion of virtual data helps to enhance the per-class accuracy in object detection and semantic segmentation. Meanwhile, it is also illustrated that the virtual data with controllable imaging conditions can be used to design evaluation experiments flexibly.

• Record URL:
  • https://doi.org/10.1109/TITS.2018.2857566
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2019, IEEE.
• Authors:
  • Li, Xuan
  • Wang, Kunfeng
  • Tian, Yonglin
  • Yan, Lan
  • Deng, Fang
  • Wang, Fei-Yue
• Publication Date: 2019-6

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 2072-2084
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 20
  • Issue Number: 6
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Computer vision; Data files; Detection and identification systems; Image analysis; Machine learning; Neural networks; Roadside; Simulation; Vehicles
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01709807
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Jul 1 2019 9:29AM