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

한국전자통신학회 (Korea Institute of Electronic Communication Science)
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차량의 부분 특징을 이용한 터널 내에서의 차량 검출 및 추적 알고리즘

A Vehicle Detection and Tracking Algorithm Using Local Features of The Vehicle in Tunnel

  • 투고 : 2013.06.07
  • 심사 : 2013.08.23
  • 발행 : 2013.08.30
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초록

본 논문에서는 터널 내에서 차량의 운행 상태를 모니터링하기 위하여 차량 검출 및 추적 알고리즘을 제안한다. 제안하는 알고리즘은 세 단계로 이루어진다. 첫 단계는 배경추정으로서 비교적 간단한 Running Gaussian Average (RGA)를 사용한다. 두 번째 단계는 차량검출 단계이며, Adaboost 알고리즘을 적용한다. 상대적으로 먼거리의 차량에 대한 오검출을 줄이기 위하여 차량의 높이별 부분 특징을 이용하여 차량을 검출한다. 물체의 부분 특징들이 임계값 이상이면 차량으로 분류한다. 마지막 단계는 차량추적 단계이며, Kalman 필터를 적용하여 이동하는 물체를 추적한다. 컴퓨터 시뮬레이션을 통하여 제안하는 알고리즘이 터널 내에서 차량 검출 및 추적에 유용한 것을 확인하였다.

In this paper, an efficient vehicle detection and tracking algorithm for detection incident in tunnel is proposed. The proposed algorithm consists of three steps. The first one is a step for background estimates, low computational complexity and memory consumption Running Gaussian Average (RGA) is used. The second step is vehicle detection step, Adaboost algorithm is applied to this step. In order to reduce false detection from a relatively remote location of the vehicles, local features according to height of vehicles are used to detect vehicles. If the local features of an object are more than the threshold value, the object is classified as a vehicle. The last step is a vehicle tracking step, the Kalman filter is applied to track moving objects. Through computer simulations, the proposed algorithm was found that useful to detect and track vehicles in the tunnel.

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참고문헌

  1. http://www.index.go.kr/egams/stts/jsp/potal/ stts/PO_STTS_IdxMain.jspidx_cd=1213&bbs= INDX_001&clas _div=A
  2. T. Vaa, M. Penttinen and I. SpyropouIou. "Intelligent transport systems and effects on road traffic accidents: state of the art", Intelligent Transport Systems, IET Vol. 1, pp. 81-88, 2007. https://doi.org/10.1049/iet-its:20060081
  3. Shee Eng Tan, Yit Kwong Chin, Bih Lii Chua and Teo, K.T.K. "Performance Analysis of Intelligent Transport Systems (ITS) with Adaptive Transmission Scheme", Computational Intelligence, Communication Systems and Networs (CICSyN), Fourth International Conference, pp. 418-423. 2012.
  4. K. Gyuyeong, K. Hyuntae, P. Jangsik, Y. Yunsik, "Vehicle Tracking Based on Kalman Filter in Tunnel", Springer ISA CCIS Vol. 200, pp. 250-256. 2011.
  5. K. Gyuyeong, K. Jaeho, K. Hyuntae, P. Jangsik, Y. Yunsik, "Vehicle Tracking Using Euclidean Distance", The Journal of the Korea Institute of Electronic Communication Sciences, Vol. 7, No. 6, pp. 1293-1299, 2012.
  6. 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.
  7. P. Jangsik, K. Hyuntae, Y. Yunsik, "Video Based Fire Detection Algorithm using Gaussian Mixture Mode", The Journal of the Korea Institute of Electronic Communication Science, Vol. 6, No. 2, pp. 206-211, 2011.
  8. C. Wren, A. Azarhayejani, T. Darrell, and A.P. Pentland, "Pfinder: real-time tracking of the human body", IEEE Trans. on Pattern Anal. and Machine Intelligence, Vol. 19, No. 7, pp. 78, 1997.
  9. D. Koller, J. Weber, T. Huang, J. malik, G. Ogasawara, B. Rao, and S. Russell, "Towards Robust Automatic Traffic Scene Analysis in Real-time", Proc. ICPR'94, pp. 126-131, Nov. 1994.
  10. Y. Freund, R.E. Schapire, "Experiments with a new boosting algorithm", In Proceedings of the IEEE International Conference of Machine Learning, pp. 148-156, 1996.
  11. P.L.M Bouttefroy, A. Bouzerdoum, S.l. Phung, A. Beghdadi, "Vehicle Tracking by non- Drifting Mean-shift using Projective Kalman Filter", Intelligent Transportation System, 2008. ITSC 11th International IEEE Conference on, pp. 61-66, Oct. 2008.