Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding

Environment perception is a critical enabler for automated driving systems since it allows a comprehensive understanding of traffic situations, which is a requirement to ensure safe and reliable operation. Among the different applications, obstacle identification is a primary module of the perception system. The authors propose a vision-based method built upon a deep convolutional neural network that can reason simultaneously about the location of objects in the image and their orientations on the ground plane. The same set of convolutional layers is used for the different tasks involved, avoiding the repetition of computations over the same image. Experiments on the KITTI dataset show that the authors' efficiency-oriented method achieves state-of-the-art accuracies for object detection and viewpoint estimation, and is particularly suitable for the recognition of traffic situations from on-board vision systems. Code is available at https://github.com/cguindel/lsi-faster-rcnn.

• Record URL:
  https://doi.org/10.1109/MITS.2018.2867526
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/19391390
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Guindel, Carlos
  • Martin, David
  • Armingol, Jose Maria
• Publication Date: 2018

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 74-86
• Serial:
  • IEEE Intelligent Transportation Systems Magazine
  • Volume: 10
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1939-1390
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5117645

Subject/Index Terms

• TRT Terms: Driver support systems; Highway safety; Machine vision; Neural networks; Proximity detectors; Task analysis; Vehicle safety
• Uncontrolled Terms: Feature extraction; Object recognition
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01684650
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 30 2018 10:47AM