DOI QR코드

DOI QR Code

Image Objects Detection Method for the Embedded System

임베디드 시스템을 위한 영상객체의 검출방법

  • 김연일 (서울시립대학교 전자전기컴퓨터 공학부) ;
  • 노승용 (서울시립대학교 전자전기컴퓨터 공학부)
  • Published : 2009.04.01

Abstract

In this paper, image detection and recognition algorithms are studied with respect to embedded carrier system. There are many suggested techniques to detect and recognize objects. But they have the propensity to need much calculation for high hit rate. Advanced and modified method needs to study for embedded systems that low power consumption and real time response are requested. The proposed methods were implemented using Intel(R) Open Source Computer Vision Library provided by Intel Corporation. And they run and tested on embedded system using a ARM920T processor by cross-compiling. They showed 1.6sec response time and 95% hit rate and supported the automated moving carrier system smoothly.

Keywords

References

  1. Y. Ming-Hsuan, D. J. Kriegman, and N. Ahuja, 'Detecting faces in images : a survey,' Pattern Analysis and Machine Intelligence, IEEE Transactions, vol 24,no, 1, pp. 34-58, Jan 2002 https://doi.org/10.1109/34.982883
  2. 박재표, 이광형, 이종희, 전문석, '객체 추출 및 추적을 이용한 실시간 웹기반 영상 감시 시스템,' 전자공학회 논문지, 제41권 CI편, 제4호, pp. 85-94, 2004
  3. B. Froba and C Kublbeck, 'Orientation template matching for face localization in complex visual scenes,' lmage Processing, 2000. Proceedings. 2000 International Conference, vol 2, (10-13) pp. 251-254. 2000 https://doi.org/10.1109/ICIP.2000.899291
  4. Rainer Lienhart and Jochen Maydt 'An extended set of haar-like features for rapid object detection,' lmage Processing. 2002. Proceedings. 2002 International Conference, vol 1, pp, 900-903, Sep, 2002, https://doi.org/10.1109/ICIP.2002.1038171
  5. Paul Viola and Michael J. Jones. 'Rapid object detection using a boosted cascade of simple features,'Conference on CVPR, IEEE, vol 1, pp. 511-518, 2001 https://doi.org/10.1109/CVPR.2001.990517
  6. G. Francois, L Eberhard, and Takashi Iwamoto, Kazuo Kyuma, Nobuyuki Otsu, 'Face recognition system using local autocorrelations and multiscale integration,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol 18, no. 10, pp. 1024-1028, Oct 1996 https://doi.org/10.1109/34.541411
  7. T. V. Pham, M. Worring, and A W. M. Sneulders, 'Face detection by aggregated bayesian network classiers,' Elsevier, Pattern Recognition Letters, vol 23, no, 4, pp. 451-461, Feb, 2002 https://doi.org/10.1016/S0167-8655(01)00177-5
  8. Rafael C Gonzalez, Richard E. Woods, 'Digital image processing,' 2nd Edition, Prentice Hall, 2001, Chapter 2.4
  9. G. Bradski, A Kaehler, and V. Pisarevsky, 'Learningbased computer vision with intel's open source. computer vision library' Intel Technology Journal, vol 9, no. 2, pp, 119-130, May 2005
  10. E. Hjelm and B. K. Low, 'Face detection: a survey,' Computer Vision and lmage Understanding, vol. 83, no, 3, pp, 236-274, 2001 https://doi.org/10.1006/cviu.2001.0921
  11. F. Crow, 'Summed-area tables for texture mapping,'11th Proceedings of conference on Computer Graphics and Interactive Techniques, SIGGRAPH, vol 18, no. 3, pp, 207-212, Jul 1984
  12. F. Fleuret and D. Geman, 'Coarse-to-Fine face detection.' Int. J. Computer Vision, voL 41, no. 1-2, pp. 85-107, Jan, 2001 https://doi.org/10.1023/A:1011113216584
  13. William T. Freeman and Edward H, Adelson, 'The design and use of steerable filters,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no, 9, pp, 891-906, Sep, 1991 https://doi.org/10.1109/34.93808
  14. H. Greenspan, S, Belongie, R. Goodman, p, Perona, S, Rakshit, and CH. Anderson, 'Overcomplete steerable pyramid filters and rotation invariance,' In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 222-228, Jun. 1994 https://doi.org/10.1109/CVPR.1994.323833