Traffic sign detection from lower-quality and noisy mobile videos

Koray Ozcan, Senem Velipasalar, Anuj Sharma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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
Pages15-20
Number of pages6
VolumePart F132201
ISBN (Electronic)9781450354875
DOIs
StatePublished - Sep 5 2017
Event11th International Conference on Distributed Smart Cameras, ICDSC 2017 - Stanford, United States
Duration: Sep 5 2017Sep 7 2017

Other

Other11th International Conference on Distributed Smart Cameras, ICDSC 2017
CountryUnited States
CityStanford
Period9/5/179/7/17

Keywords

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

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Traffic sign detection from lower-quality and noisy mobile videos'. Together they form a unique fingerprint.

  • Cite this

    Ozcan, K., Velipasalar, S., & Sharma, A. (2017). Traffic sign detection from lower-quality and noisy mobile videos. In ICDSC 2017 - 11th International Conference on Distributed Smart Cameras (Vol. Part F132201, pp. 15-20). Association for Computing Machinery. https://doi.org/10.1145/3131885.3131925