DOI QR코드

DOI QR Code

Improve the Performance of People Detection using Fisher Linear Discriminant Analysis in Surveillance

서베일런스에서 피셔의 선형 판별 분석을 이용한 사람 검출의 성능 향상

  • Kang, Sung-Kwan (HCI Lab., Department of Computer and Information Engineering, Inha University) ;
  • Lee, Jung-Hyun (Department of Computer and Information Engineering, Inha University)
  • Received : 2013.10.22
  • Accepted : 2013.12.20
  • Published : 2013.12.28

Abstract

Many reported methods assume that the people in an image or an image sequence have been identified and localization. People detection is one of very important variable to affect for the system's performance as the basis technology about the detection of other objects and interacting with people and computers, motion recognition. In this paper, we present an efficient linear discriminant for multi-view people detection. Our approaches are based on linear discriminant. We define training data with fisher Linear discriminant to efficient learning method. People detection is considerably difficult because it will be influenced by poses of people and changes in illumination. This idea can solve the multi-view scale and people detection problem quickly and efficiently, which fits for detecting people automatically. In this paper, we extract people using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected people. The purpose of this paper is to classify people and non-people using fisher linear discriminant.

Keywords

People Detection;Feature Extraction;Vectorization;Fisher Linear Discriminant

Acknowledgement

Supported by : 인하대학교

References

  1. Balakrishnama, S.; Ganapathiraju, A.; Picone, J.; "Linear discriminant analysis for signal processing problems," Southeastcon '99. Proceedings. IEEE, pp: 78-81, March.1999
  2. Zhao, W.; Chellappa, R.; Krishnaswamy, A.; "Discriminant analysis of principal components for face recognition," Third IEEE International Conference on, 14-16 , pp336-341, April.1998
  3. Ordowski, M.; Meyer, G.G.L.; "Geometric linear discriminant analysis," ICASSP '01. 2001 IEEE International Conference on, Vol. 5, 7-11, pp.3173-3176 , May 2001
  4. Hyun-Chul Kim; Daijin Kim; Sung Yang Bang; "Face recognition using LDA mixture model," Pattern Recognition, 2002. Proceedings. 16th International Conference on , 11-15, 8. 2002. pp: 486-489 vol.2
  5. Liu. Chengjun ; H.Wechsler.; "Enhanced Fisher linear discriminant models for face recognition," Pattern Recognition, 1998. Proceedings. Vol. 2, 16-20 pp: 1368-1372, Aug.1998
  6. Papageorgiou, C.; Poggio, T.; "Trainable pedestrian detection," Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on , Vol. 4, 24-28 pp:35-39 , Oct. 1999
  7. Takano, S.; Niijima, K.; Abdukirim, T.; "Fast face detection by lifting dynamic wavelet filters," Image Processing, International Conference on , Vol.3, pp. 893-896, Sept. 14-17.2003
  8. C. W. Ng and S. Ranganath, "Real-time Gesture Recognition System and Application," Image and Vision Computing, Vol. 20, Issues 13-14, pp. 993-1007, 2002. https://doi.org/10.1016/S0262-8856(02)00113-0
  9. P. Phillips, "The FERET Database and Evolution Procedure for Object Recognition Al-gorithms," Image and Vision Computing, Vol. 16, No. 5, pp. 295-306, 1999.
  10. Lee, W.S.; Lee, H.J.; Chung, J.H.; "Wavelet-based FLD for face recognition," Circuits and Systems, Proceedings of the 43rd IEEE Midwest Symposium on , Vol.2, 8-11 pp. 734-737, Aug. 2000
  11. H. Schneiderman and T. Kanade, "Object Detection Using the Statistics of Parts," Int'l J. Computer Vision, Vol. 56, No. 3, pp. 151-177, 2004. https://doi.org/10.1023/B:VISI.0000011202.85607.00

Cited by

  1. A standardization model based on image recognition for performance evaluation of an oral scanner vol.9, pp.6, 2017, https://doi.org/10.4047/jap.2017.9.6.409