Real-time vision-based multiple vehicle detection and tracking for nighttime traffic surveillance

Yen Lin Chen*, Bing-Fei Wu, Chung Jui Fan

*Corresponding author for this work

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

40 Scopus citations

Abstract

This study presents an effective system for detecting and tracking moving vehicles in nighttime traffic scene for traffic surveillance. The proposed method identifies vehicles based on detecting and locating vehicle headlights and taillights by using the techniques of image segmentation and pattern analysis. First, to effectively extract bright objects of interest, a fast bright-object segmentation process based on automatic multilevel histogram thresholding is applied on the nighttime road-scene images. This automatic multilevel thresholding approach can provide robustness and adaptability for the detection system to be operated well under various illumination conditions at night. The extracted bright objects are processed by a spatial clustering and tracking procedure by locating and analyzing the spatial and temporal features of vehicle light patterns, and then identifying and classifying the moving cars and motorbikes in the traffic scenes. Experimental results demonstrate that the proposed approach is feasible and effective for vehicle detection and identification in various nighttime environments for traffic surveillance.

Original languageEnglish
Title of host publicationProceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
Pages3352-3358
Number of pages7
DOIs
StatePublished - 1 Dec 2009
Event2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 - San Antonio, TX, United States
Duration: 11 Oct 200914 Oct 2009

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
CountryUnited States
CitySan Antonio, TX
Period11/10/0914/10/09

Keywords

  • Intelligent transportation systems
  • Nighttime surveillance
  • Traffic surveillance
  • Vehicle detection
  • Vehicle tracking

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