Home > Archive > 2014 > Volume 4 Number 6 (Dec. 2014) >
IJMLC 2014 Vol. 4(6): 516-521 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V6.465

Blind-Spot Vehicle Detection Using Motion and Static Features

Din-Chang Tseng, Chang-Tao Hsu, and Wei-Shen Chen

Abstract—When driving a vehicle on a road, if a driver want to change lane, he must glance the rear and side mirrors of his vehicle and turn his head to scan the possible approaching vehicles on the side lanes. However, the view scope by the above behavior is limited; there is a blind spot area invisible. To avoid the possible traffic accident during lane change, we here propose a lane change assistance system to assist changing lane. Two cameras are mounted under side mirrors of the host vehicle to capture rear-side-view images for detecting approaching vehicles. The proposed system consists of four stages: estimation of weather-adaptive threshold values, optical flow detection, static feature detection, and detection decision. The proposed system can detect side vehicles with various approaching speed; moreover, the proposed system can also adapt variant weather conditions and environment situations. Experiment with 14 videos on eight different environments and weather conditions, the results reveal 96 % detection rate with less false alarm.

Index Terms—Advanced driver assistance system, blind spot detection, optical flow, underneath shadow features.

Din-Chang Tseng, Chang-Tao Hsu, and Wei-Shen Chen are with the Institute of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan 32001 (e-mail: tsengdc@ip.csie.ncu. edu.tw, justinhsuncu@gmail.com, easywine2@gmail.com).


Cite: Din-Chang Tseng, Chang-Tao Hsu, and Wei-Shen Chen, "Blind-Spot Vehicle Detection Using Motion and Static Features," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 516-521, 2014.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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