5.2 動態人與非人之辨認
5.2.2 動態正面資訊
本實驗的操作方式，是假設我們所觀測的場景中，有一移動中的人，以及一 個緩緩移動的人形看板。可能發生的情況是人形看板底下架設軌道，以滑行的方 式朝向或是遠離攝影機。因為器材取得不易，所以我們使用的方式是以人拿著人 形看板，將自己的身體隱藏在其後，儘量以不晃動的方式讓人形看板移動。這個 實驗的目的主要是在找出當人以正面移動時所具有的運動特徵。 圖 5-13 為觀測 場景的背景圖、前景圖以及相減之後的灰階輸出結果。
(a)背景
一
(c)灰階結果
(b)前景
圖. 5-13: 動態正面前景、背景與前處理結果
我們同樣利用連續 30 張的畫格，將其中的移動物體分離出來，從中擷取移 動中的人以及人形看板，分析其運動特徵。觀察的重點在 4.2 中提到，是經過正 規化的圖形，其長度 70 至 90 的地方。圖 5-14 中，(a)為正面行走的人的分離影 像，經過正規化處理後的結果。因為此人為面對攝影機的方向走來。起始的位置 是左腳在前，右腳在後，之後跨出右腳。就本實驗方法，質心會向左邊，也就是
離原點較近的地方作移動，之後跨出左腳時，質心再往遠離原點的方向移動。圖 5-14(b)中確實顯示了此現象。圖 5-15 為有人拿著看板緩緩向前移動的連續 30 張經過正規化的分離影像。因為看板外型的關係，背後拿著的人的雙腳在細部移 動的過程中，造成質心會發生小小的變化情形。理論上來說，如果只有看板在移 動的話，而不考慮突然間的光度變化，則看板紅色區域的質心應該不會發生變 化。但在同樣量測的刻度座標上來看，雖然腳步的細碎移動影響了結果，但變化 的幅度仍舊較一般人以正常步伐行走時要來的小，也證明了此運動特徵是可以用 來作判別的，而且不受人是否提著手提包或是會影響寬度資訊的物體的影響。
(a)人正面行走之正規化連續影像
(b)紅色區域之質心變化 圖. 5-14: 人正面處理結果
(a)人形看板正面之正規化連續影像
(b)紅色區域質心移動情形 圖. 5-15: 人形看板正面處理結果
第六章
成份，在較為複雜的環境中對於人與非人也能夠準確地做判斷。
（四）在動態追蹤的人辨認方面，提出了新的想法，可利用在未來更進一 步的研究當中。
本實驗系統的應用層面廣泛。在防盜系統的使用中，我們可以利用此系統 偵測小偷的闖入，並發出警報警示。在對於人數需要做控制的地分，本系統也能 夠計算出場景中的人數多寡。
雖然本實驗系統在辨別人與非人上的效果良好，但仍舊存在著一些需要克 服的缺點。我們的攝影機是以固定的方式架設著。雖然對於一些輕微的樹葉晃動 以及陰影的雜訊可以經過一些前處理加以過濾，然而，攝影機的輕微晃動或是所 觀測場景發生改變將會產生嚴重的影響。此缺點可利用定期的背景更新作為解決 的方式。另外，因為我們所選取的訓練樣本都是人以側向或是直向方式行走的樣 本，對於觀測場景中若是存在著人以爬行或是半蹲等其他姿勢的話，則辨別不出 來。一個改進的方式是可以將此圖像加入訓練樣本中，不過可能會與其他非人的 影像，如狗等發生形體相近的情況，而增加判別的困難度。當人發生重疊或是彼 此距離接近時會發生誤判的情況，可能就需要一些形態分析才能加以解決。
在動態的判斷方面，一但發生重疊的情形，則連通成份的技術會將重疊的 兩個物體當成同一個，以致於發生無法判斷的情形，要等到重疊的物體分離之 後，才能重新做判斷。
參考文獻
[1] P. J. Burt, and J. R. Bergen, “Object tracking with a moving camera: An application of dynamic motion analysis,” in Proceedings IEEE Conference Workshop Visual Tracking, 1989, pp. 2-12.
[2] P. H. Batavia, D. E. Pomerleau, and C. E. Thorpe, ”Overtaking vehicle detection using implicit optical flow,” in 1997 IEEE Conference on Intelligent Transportation System, 1997, pp. 729-734.
[3] W. J. Gillner, “Motion based vehicle detection on motorways,” in Proceedings of the Intelligent Vehicles '95 Symposium, 1995, pp.483-487.
[4] T. Darrell, G. Gordon, M. Harville, and J. Woodfill, “Integrated person tracking using stereo, color, and pattern detection,” in 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998, pp. 601-608.
[5] L. Zhao, and C. E. Thorpe, “Stereo- and neural network-based pedestrian detection,” IEEE Trans. Intelligent Transportation Systems, vol. 1, pp. 148-154, Sept. 2000.
[6] C. E. Smith, C. A. Richards, S. A. Brandt, and N. P. Papanikolopoulos, “Visual tracking for intelligent vehicle-highway systems,” IEEE Trans. Vehicular Technology, vol. 45, no. 4, pp. 744-759, Nov. 1996.
[7] C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: real-time tracking of the human body,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, pp. 780-785, July 1997.
[8] J. Segen, and S. Pingali, “A camera-based system for tracking people in real-time,” in Proceedings 13th International Conference on Pattern Recognition, 1996, pp.63-67.
[9] K. Rohr, “Toward model-based recognition of human movements in image sequences,” Computer Vision, Graphics and Image Processing, Image Understanding, vol.59, no.1, pp.94-115, Jan. 1994.
[10] D. M. Gavrila, and V. Philomin, “Real-time object detection for ’smart’
vehicles,” in The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, 1999, pp. 87-93.
[11] A. Broggi, M. Bertozzi, A. Fascioli, and M. Sechi, “Shape-based pedestrian detection,” in Proceedings of the IEEE Intelligent Vehicles Symposium, 2000, pp.
215-220.
[12] M. Oren, C. Papageorgiou, P. Sinha, E. Osuna and T. Poggio, “Pedestrian Detection Using Wavelet Templates,” in Proceedings of Conference on Computer Vision and Pattern Recognition, 1997.
[13] C. Papageorgiou, and T. Poggio, “Trainable pedestrian detection,” in 1999 International Conference on Image Processing, vol.4, 1999, pp. 35-39.
[14] D. Toth and T. Aach, “Detection and Recognition of Moving Objects using Statistical Motion detection and Fourier Descriptors,” in 12th International Conference on Image Analysis and Processing, pp. 430-435, 2003.
[15] B. Heisele and C. Wohler, “Motion-Based Recognition of Pedestrians,” in International Conference on Pattern Recognition, vol.2, pp. 1325-1330, 1998.
[16] H. Mori, N. Charkari and T. Matsushita, “On-Line Vehicle and Pedestrian Detections Based on Sign Pattern,” in IEEE Trans. On Industrial Electronics, vol.
41, No.4, 1994.
[17] S. Yasutomi, and H. Mori, “A method for discriminating of pedestrian based on rhythm,” in IROS '94. Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems '94. 'Advanced Robotic Systems and the Real World', vol. 2, 1994, pp. 988 –995.
[18] A. Utsumi and N. Tetsutani, “Human Detection using Geometrical Pixel Value Structures,” in Proceeding of the 5th IEEE international Conference on Automatic Face and Gesture Recognition, 2002.
[19] L. D. Stefano and A. Bulgarelli, “A Simple and Efficient Connected Components Labeling Algorithm,” in International Conference on Image Analysis and
Processing, 1999.
[20] Lindsay I Smith, “A tutorial on Principle Components Analysis,”
http://csnet.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf [21] M. Turk and A. Pentland. “Face Recognition Using Eigenfaces,” in Proceedings
of IEEE Computer Vision and Pattern Recognition, pp. 586--590, Maui, Hawaii, Dec. 1991.
[22] M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, vol.3, no. 1, pp. 71-86, 1991.
[23] P. Viola and M. Jones, “rapid object detection using a boosted cascade of simple features,” IEEE Conf. on Computer Vision and Pattern Recognition, 2001.
[24] O. Javed, S. Ali and M. Shah, “Online Detection and Classification of Moving Objects Using Progressively Improving Detectors,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.1, pp. 696-701, 2005.
[25] R. Culter and L. Davis, “Robust Real-Time Periodic Motion Detection, Analysis, and Applications,” in IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 22, No. 8, 2002.
[26] P. Viola, M. Jones and D. Snow, “Detecting Pedestrians Using Patterns of Motion and Appearance,” in Proceedings of the International Conference on Computer Vision,2003.
[27] R. Polana, and R. Nelson, “Detecting activities,” in 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1993, pp. 2-7.
[28] D. Zhang and G. Lu, “Content-Based Shape Retrieval Using Different Shape Descriptors: A Comparative Study,” IEEE International Conference on Multimedia and Expo, 2001.
[29] 葉怡成，類神經網路模式應用與實作，儒林出版社，2002.
[30] C. Curio, J. Edelbrunner, T. Kalinke, C. Tzomakas, and W. von Seelen, “Walking
pedestrian recognition,” IEEE Trans. Intelligent Transportation Systems, vol. 1, issue 3, pp.155-163, Sept. 2000.
[31] H. Roh, S. Kang and S-W Lee, “Multiple People Tracking Using an Appearance Model Based on Temporal Color,” in 15th International Conference on Pattern Recognition (ICPR'00) - Volume 4, 2000.
[32] D. Zhang and G. Lu, “A Comparative Study of Fourier Descriptors for Shape Representation and Retrieval,” the 5th Asian Conference on Computer Vision, Jan.
2002.
[33] I. Haritaoglu, D. Harwood and S. Davis, “W4: Real-Time Surveillance of People and Their Activities,” IEEE Trans. On Pattern and Machine Intelligence, Vol.22, No.8, Aug. 2000.
[34] I. Haritaoglu, D. Harwood and S. Davis, “Backpack: Detection of People Carrying Objects Using Silhouettes,” IEEE International Conference on Computer Vision, pp. 102-107, 1999.
[35] I. Haritaoglu, D. Harwood and S. Davis, “Hydra: Multiple People Detection and Tracking Using Silhouettes,” In Second Workshop of Visual Surveillance at CVPR, pages 6-13, 1999.
[36] T. Zhao and R. Nevatia, “Tracking Multiple Human in Complex Situations,”
IEEE Trans. On Pattern and Machine Intelligence, vol. 26, No. 9, Sep, 2004.
[37] S. J. McKenna, S. Jabri, Z. Duric, H. Wechsler and A. Rosenfeld, “Tracking Groups of People,” Comput. Vision Image Understanding, no. 80, pp. 42--56, 2000.
[38] Y. Kuno, T. Watanabe, Y. Shimosakoda and S. Nakagawa, “Automated detection of Human for Visual Survillance System,” in IEEE Proceedings of ICPR, 1996.
[39] M. Riesenhuber and T. Poggio, “Models of Object Recognition,” in Nature Neuroscience Supplement, vol. 3, Nov, 2000.
[40] I. Sekita, T. Kurita and N. Otsu, “Complex Autoregressive Model for Shape Recognition,” in IEEE Trans. On Pattern Analysis and Machine Intelligence, vol.
14, No4, Apr, 1992.
[41] H. Kauppinen, T. Seppanen and M. Pietikainen, “An Experimental Comparision of Autoregressive and Fourier-Based Descriptors in 2D Shape Classification,” in IEEE Tran. On Pattern Analysis and Machine Intelligence, Vol. 17, No. 2, Feb, 1995.
[42] L. Wang, T. Tan, W. Hu and H. Ning, “Automatic Gait Recognition Based on Statistical Shape Analysis,” in IEEE Trans. On Image Processing, vol. 12, No. 9, Sep, 2003.