The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
권호 표지
DOI QR코드

DOI QR Code

A Study for Video-based Vehicle Surveillance on Outdoor Road

실외 도로에서의 영상기반 차량 감시에 관한 연구

  • Received : 2013.09.25
  • Accepted : 2013.11.15
  • Published : 2013.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Detection performance of the vehicle on the road depends on weather conditions, the shadow by the movement of the sun, or illumination changes, etc. In this paper, a vehicle detection system in conjunction with a robust background estimate algorithm to environment change on the road in daytime is proposed. Gaussian Mixture Model is applied as background estimation algorithm, and also, Adaboost algorithm is applied to detect the vehicle for candidate region. Through the experiments with input videos obtained from a various weather conditions at the same actual road, the proposed algorithm were useful to detect vehicles in the road.

실외 도로에서의 차량 검출 성능은 기상 상태, 태양 이동에 의한 그림자, 조도 변화 등에 영향을 받는다. 본 논문에서는 낮 시간대의 실외도로에서 이러한 주변 환경변화에 강건한 배경 추정 알고리즘과 연동한 차량 검출 시스템을 제안한다. 배경 추정 알고리즘은 혼합 가우시안 모델을 적용하고 후보 영역에 대한 차량 검출은 Adaboost 알고리즘을 적용하였다. 흐린 날, 비오는 날 등 동일한 실제 도로에서 서로 다른 기후에 획득한 CCTV 비디오 영상을 사용한 실험을 통해 제안하는 방법이 일반 도로에서의 차량 검출에 유용한 것을 확인하였다.

Keywords

References

  1. Saka, A. A. "Intelligent Transportation Systems (ITS) Education and Training : Why Progress is Slow", Third World Congress on Intelligent Transportation Systems, Orlando, Florida. 1996.
  2. Tamer Nadeem, Sasan Dashtinezhad, Chunyuan Liao, and Liviu Iftode, "TrafficView: A Scalable Traffic Monitoring System", IEEE International Conference on Mobile Data Management, pp. 13-26, 2004.
  3. Iwasaki, Y. and Kurogi, Y., "Real-time robust vehicle detection through the same algorithm both day and night", International Conference on Wavelet Analysis and Pattern Recognition, pp. 1008-1014, 2007.
  4. Zehang Sun, Bebis, G., and Miller, R., "Onroad vehicle detection: a review", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 3, No. 5, pp. 694-711, 2006.
  5. Bao, Jingang Ying, Jiangqian Hayamizu, Satoru, "Development of traffic analysis system using GIS", The 2-nd International Conference on Information Science and Engineering (ICISE), pp. 3648-3652, 2010.
  6. Haixiang Zou Yang Yue Qingquan Li Yonghui Shi, "A spatial analysis approach for describing spatial pattern of urban traffic state", The 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 557-562, 2010.
  7. K. Gyuyeong, K. Jaeho, K. Hyuntae, P. Jangsik, Y. Yunsik, "Vehicle Tracking Using Euclidean Distance", The journal of the Korea Institute of Electronic Communication Science, Vol. 7, No. 6, pp. 1293-1299, 2012.
  8. K. Gyuyeong, K. Hyuntae, P. Jangsik, Y. Yunsik, "Vehicle Tracking Based on Kalman Filter in Tunnel", Springer ISA 2011, CCIS vol. 200, pp. 250-256. 2011.
  9. K. Hyuntae, L. Geunhoo, P. Jangsik, Y. Yunsik, "Vehicle Detection in Tunnel using Gaussian Mixture Model and Mathematical MorphologicalProcessing", The Journal of the Korea Institute of Electronic Communication Science, Vol. 7, No. 5, pp. 967-974, 2012.
  10. C. Stauffer and W.E.L. Grimson, "Adaptive background mixture models for real-time tracking", Proc.IEEE CVPR 1999, pp. 246-252, June, 1999.
  11. Massimo Piccardi, "background subtraction techniques a review", 2004 IEEE International Conference on Systems, Man and Cybernetics, pp. 3099-3104, 2004
  12. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features", Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, 2001.
  13. N. N. Dauoud, B. B. Samir, J. Janier, "Fast Template Matching Method Based Optimized Sum of Absolute Difference Algorithm for Face Localization," International Journal of Computer Applications, Vol. 18, No. 8, 2011.

Cited by

  1. Vocal Separation Using Selective Frequency Subtraction Considering with Energies and Phases vol.20, pp.3, 2015, https://doi.org/10.5909/JBE.2015.20.3.408