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Human detection in video sequences using stereo vision
|關鍵字:||Stereo Vision;立體視覺;Calibration;Video Sequence;Human Detection;校正;視訊影像;人體偵測||出版社:||電機工程學系所||引用:|| D. Schulz, W. Burgard, D. Fox, and A. Cremes, “Tracking multiple moving objects with a mobile robot,” Proc. IEEE CVPR, vol.1, pp.371-377, 2001.  D. A. Forsyth, and J. Ponce, Computer vision: a modern approach, Prentice Hall, 2002.  R. Collins, C. J. Taylor, and J. Graham, “Special issue on video surveillance and monitoring,” IEEE Trans. Pattern Anal. and Machine Intell., vol. 22, pp. 745-746, 2000.  D. M. Gvarila, “The visual analysis of human movement: a survey,” Computer Vision and Image Understanding, vol. 73, pp. 82-98, Jan. 1999.  V. Pavlovic, R. Sharma, and T. S. Huang, “Visual interpretation of hand gestures for human computer interaction: a review,” IEEE Trans. Pattern Anal. and Machine Intell., vol. 19, no. 7, pp. 677-695, Jul. 1997.  Y. Wu, and T. Yu, “A filed model for human detection and tracking,” IEEE Trans. Pattern Anal. and Machine Intell., vol. 28, no. 5, pp. 753-765, May 2006.  S. Ioffe, and D. A. Forsyth, “Probabilistic methods for finding people,” IJCV, 43(1), pp. 45-68, 2001.  A. Mohan, C. Papageorgiou and T. Poggio, “Example-based object detection in Images by components,” IEEE Trans. PAMI, 23(4), pp. 349-361, 2001.  Christophe Simon, Frederique Bicking and Thierry Simon, ”Estimation of depth on thick edges from sharp and blurred images,” IEEE Instrumentation and Measurement Technology Conference, vol. 1, pp. 323-328.  Cassandra Swain, Alan Peters and Kazuhiko Kawamura, ”Depth estimation from image defocus using fuzzy logic,” Proceedings of the 3rd IEEE International Conference on Fuzzy System, vol. 1, P9, 94-99, 1994.  F. Deschen, D. Ziou, P. Fuchs, ”Homotopy-based estimation of depth cues in spatial domain,” IEEE International Conference on Pattern Recognition, vol. 3, pp. 627-630, 2002.  F. Deschen, D. Ziou, ”Homotopy-based computation of defocus blur and affine transform,” in Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'03), vol. 1, pp. I-398-404, 2003.  L. Li, Y. T. Koh, S. S. Ge, and W. Huang, “Stereo-based human detection for mobile service robots,” Proc. IEEE Conferecne on Control, Automation, Robotics, and Computer Vision, pp. 74-79, Dec. 2004.  L. Li, S. S. Ge, T. Sim, Y. T. Koh, and X. Hunag, “Object-oriented scale-adaptive filtering for human detection from stereo images,” Proc. IEEE Conference on Cybernetics and Intelligent Systems, pp. 135-140, Dec. 2004.  D.C. Brown, “Decentering Distortion of Lenses,” Photogrammetric Eng., vol. 32, No. 3, pp. 444-462, May 1966.  Roger Y. Tsai, “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and Lenses,” IEEE Journal of Robotics and Automation, vol. ra-3, No.4, Aug. 1987.  Z. Zhang, “Flexible camera calibration by viewing a plane from unknown orientations,” Proceedings of the IEEE International Conference on Computer Vision, vol. 1, pp. 666-673, 1999.  Q.T. Luong and O.D. Faugeras, “The fundamental matrix: theory, algorithms, and stability analysis,” route des Lucioles, B.P. 93, 2004.  K. Konolige, “Small vision system: hardware and implementation,” Proc. Int'l Symp. Robotics Research, pp. 111-116, Aug. 1997.  Deoffrey Egnal and Richard P. Wildes, “Detecting binocular half-occlusion: empirical comparisons of five approaches,” IEEE transactions on pattern analysis and machine intelligence, vol. 24, No. 8, 2008.  T. Lindegerg, “Scale-space theory: a basic tool for analyzing structures at different scales,” Jour. of App. Stat., 21(2), pp. 225-270, 1994.  S. Smith, and J. Brady, “SUSAN-a new approach to low level image processing,” IJCV, 23(1), pp. 45-78, 1997.||摘要:||
In this thesis, we focus on the detection of human bodies using two cameras. We can roughly divide this research into two steps. The first one is to obtain the stereo disparity images, which includes the calibration of cameras as preprocessing. The second step is to detect the human regions accurately and efficiently. We extract the human regions in the images by using the fuzzy 2D histogram on the X-D plane. Then we used a scale-adaptive filter to enhance the area with an average scale for human bodies. According to the experiment results, our method can produce a segmentation region of the human shape which contains a human object in the image. And we had tracked the movements of human objects in the video sequences.
|Appears in Collections:||電機工程學系所|
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