Vehicle Tracking at Nighttime by Kernelized Experts With Channel-Wise and Temporal Reliability Estimation

Despite the fact that in recent years, vision-based tracking approaches have made significant progress, the task of tracking vehicles at night still remains challenging. Visual information is strongly deteriorated or at least degraded due to poor illumination conditions. This reduces the perceptive ability of vision systems significantly and can even lead to target loss, resulting in false estimation and/or false prediction of object behavior. In this paper, the authors propose a novel online-learning method to track vehicles at night. Their method is based on the kernelized correlation filter and assembles different feature channels to kernelized experts. By estimating their reliabilities, they force the appearance model to focus on the most discriminative visual features to accomplish the classification. In addition, a temporal optimization step in conjunction with a memory model is used to remove outliers and keep the most reliable samples to train the tracker models. Experiments over various daytime and weather conditions show that their approach outperforms existing trackers at night and in case of bad weather while offering state-of-the-art performance in more favorable situations. As their tracker has only little computational cost, it is appropriate for use cases with real-time requirements like in automotive or industrial applications.

• Record URL:
  • https://doi.org/10.1109/TITS.2017.2771410
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Tian, Wei
  • Chen, Long
  • Zou, Ke
  • Lauer, Martin
• Publication Date: 2018-10

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 3159-3169
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 19
  • Issue Number: 10
  • 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; Image analysis; Mathematical models; Night visibility; Night vision; Tracking systems; Wet weather
• Uncontrolled Terms: Robustness
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01684726
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Oct 30 2018 3:49PM