The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지

Vehicle Detection and Classification Using Textural Similarity in Wavelet Domain

웨이브렛 영역에서의 질감 유사성을 이용한 차량검지 및 차종분류

  • 임채환 (경북대학교 전자공학과 영상통신연구실 정회원) ;
  • 박종선 (LG전자 정회원) ;
  • 이창섭 (LG전자 정회원) ;
  • 김남철 (경북대학교 전자공학과 영상통신연구실 정회원)
  • Published : 1999.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

We propose an efficient vehicle detection and classification algorithm for an electronic toll collection using the feature which is robust to abrupt intensity change between consecutive frames. The local correlation coefficient between wavelet transformed input and reference images is used as such a feature, which takes advantage of textural similarity. The usefulness of the proposed feature is analyzed qualitatively by comparing the feature with the local variance of a difference image, and is verified by measuring the improvements in the separability of vehicle from shadowy or shadowless road for a real test image. Experimental results from field tests show that the proposed vehicle detection and classification algorithm performs well even under abrupt intensity change due to the characteristics of sensor and occurrence of shadow.

본 논문에서는 간단히 한국통신학회본 논문에서는 웨이브렛 영역에서의 질감 유사성을 특징으로 사용함으로써 프레임간의 급격한 밝기변화에 강건한 특성을 가지는 툴게이트 과금을 위한 차량검지 및 차종분류 알고리듬을 제안하였다. 질감의 유사성을 나타내는 특징으로는 웨이브렛 변환된 입력영상과 배경영상 간의 국부상관계수를 이용하였다. 기존의 차량검지에서 사용되었던 특징인 차영상에 대한 분산과 비교하여 제안된 특징의 유용성을 정상적으로 분석하였으며, 실제 테스트 영상에 대하여 차량과 그림자가 관측되거나 관측되지 않는 도로와의 구분 용이성 정도를 측정함으로써 제안된 특징의 우수성을 보인다. 현장 테스트에 대한 실험 결과는 제안된 차량검지 및 차종분류 알고리듬이 센서의 특성과 그림자의 발생에 의한 프레임 간의 급격한 밝기 변화와 같은 상황하에서도 매우 안정적이며 우수한 성능을 보이는 것을 확인할 수 있다.

Keywords

References

  1. Systems architecture definitions ITS America
  2. IEEE Trans. on Vehicular Tech. v.VT-29 no.2 Traffic control systems hardware A. A. Cimento
  3. IEEE Trans. Vehicular Tech. v.VT-29 no.2 A sensor for control of arterials and networks E. E. Hilbert;P. A. Rennie;W. A. Kneidl
  4. IEEE Trans. Vehicular Tech. v.IE-232 no.3 Traffic monitoring and control using machine vision: survey R. M. Inigo
  5. IEEE Trans. on Vehicular Tech. v.38 no.3 Application on machine vision to traffic monitoring and control R. M. Inigo
  6. Vehicle detection through image processing for traffic surveillance and control P. G. Michalopoulos(et al.)
  7. IEEE Trans. Vehicular Tech v.40 no.1 Vehicle detection video through image processing: The Autoscope system P. G. Michalopoulos
  8. Proc. the 3rd Int. Conf. Road Traffic Control, IEEE-IEE Road traffic monitoring using image processing A. Rourke;M. G. H. Bell;N. Hoose
  9. IEEE Trans. on Vehicular Tech. v.40 no.1 The Los Angeles automated traffic surveillance and control (ATSAC) system E. Rowe
  10. Proc. IEEE-IEE VNIS'93 New techniques for digital CCTV processing in automatic traffic monitoring R. J. Blissett;V. Stennett;R. M. Day
  11. IEEE Trans. on Vehicular Tech. v.42 no.4 Overhead infrared sensor for monitoring vehicular traffic T. M. Hussain;T. N. Saadawi;S. A. Ahmed
  12. IEEE Trans. on Vehicular Tech. v.43 no.1 Active-infrared overhead vehicle sensor R. A. Olson;R. L. Gustavson;R. J. Wangler;R. E. McConnell, II
  13. Signal Processing v.31 no.2 Statistical model-based change detection in moving video T. Aach;A. Kaup;R, Mester
  14. Proc. ICPR'96 v.3 Illumination assessment for vision-based traffic monitoring L. Wixson
  15. ICIP'97 v.1 A fast algorithm for target shadow removal in monocular colour sequences P. Gamba;M. Lilla
  16. International Journal of Computer Vision v.10 no.3 Model-based object tracking in monocular image sequences of road traffic scenes D. Koller;K Daniilidis;H. H. Nagel
  17. Opt. Eng. v.35 no.6 Model-based vehicle tracking from image sequences with an application to road surveillance W. Y. Kan;J. V. Krogmeier;P. C. Doerschuk
  18. Proc. IEEE CVPR'97 A real-time computer vision system for measuring traffic parameters D. Beymer;P. McLauchlan;B. Coifman;J. Malik
  19. Proc. ICIP'97 v.2 Real time object detection, tracking and classification in monocular image sequences of road traffic scenes M. Ebbecke;M. B. H. Ali;A. Dengel
  20. Proc. IEEE ICPR'96 v.3 Traffic measurement with a roadside vision system - individual tracking of overlapped vehicles T. Ikeda;S. Ohnaka;M. Mizoguchi
  21. IEEE Trans. Patt. Anal. Machine Intell. v.14 no.7 Characterization of signals from multiscale edges S. Mallat;S. Zhong
  22. Vehicle detection algorithm using local similarities in video sequence, M.S. thesis J. S. Park
  23. Development of a Video Vehicle Detection System for Electronic Toll Collection, Final Research Report N. C. Kim