Traffic sign detection from lower-quality and noisy mobile videos

Koray Ozcan, Senem Velipasalar, Anuj Sharma

Research output: Chapter in Book/Entry/PoemConference contribution

1 Scopus citations


Accurate traffic sign detection, from vehicle-mounted cameras, is an important task for autonomous driving and driver assistance. It is a challenging task especially when the videos acquired from mobile cameras on portable devices are lowquality. In this paper, we focus on naturalistic videos captured from vehicle-mounted cameras. It has been shown that Regionbased Convolutional Neural Networks provide high accuracy rates in object detection tasks. Yet, they are computationally expensive, and often require a GPU for faster training and processing. In this paper, we present a new method, incorporating Aggregate Channel Features and Chain Code Histograms, with the goal of much faster training and testing, and comparable or better performance without requiring specialized processors. Our test videos cover a range of different weather and daytime scenarios. The experimental results show the promise of the proposed method and a faster performance compared to the other detectors.

Original languageEnglish (US)
Title of host publicationICDSC 2017 - 11th International Conference on Distributed Smart Cameras
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450354875
StatePublished - Sep 5 2017
Event11th International Conference on Distributed Smart Cameras, ICDSC 2017 - Stanford, United States
Duration: Sep 5 2017Sep 7 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F132201


Other11th International Conference on Distributed Smart Cameras, ICDSC 2017
Country/TerritoryUnited States


  • Aggregate Channel Features
  • Chain Code Histograms
  • Fast-RCNN
  • Traffic sign detection

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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