人物追蹤於環境之監控保全中扮演著很重要的角色。而本文針對於分散式架設於頭部上方攝影機之環境，以達到建築物內之行人追蹤。於此監控建築內，我們所關心持續追蹤的資訊包括各區域進入的人數，特定行人的行走軌跡與多位行人同時發生於系統環境內之事件關係。在架設於天花板的攝影機環境中，每位行人在不同的四個視角會有不同的影像輪廓，而每位行人的身份特徵即是由此四個不同視角之影像資訊組成。因此，本系統提出尋頭部影像的技術，測得該型人的移動方向，進而分析每個人的實際影像區塊範圍，最後利用色彩直條圖區分出不同的行人。當攝影機下的ROI偵測到出入事件發生，行人追蹤系統能與現有的行人身分特徵做比對，明確分析出彼此身份特徵的差異。另外，為了使得的整體系統程序時間減少，透過各區域彼此的關係減少資訊搜尋時間，以達到此目的。再者，本系統特別注意到軟體的配置，使本系統容易被使用於不同樓層設置環境中。我們已實際於台灣科技大學電機系館三樓架設本監控系統，而由監控效能分析數據可知：進出人數的計數正確率為93%，身分辨識的正確率為76%。

People tracking plays an important issue for the monitoring and surveillance of a concerned environment. This paper focuses on people tracking within a building with the installment of distributed overhead cameras. Our primary concern is to keep tracks of: number of people entrance to a particular area; whereabouts trajectory of a particular person; and simultaneous presence relation of several people at a particular area within the monitored building. With image taken from ceiling mounted camera, pedestrian’s physiologic contour is analyzed from four difference viewing angles to form a person’s identity signature. In doing so, techniques to locate a person’s head, to predict his/her movement direction, to separate overlapped physiological blobs, and to differentiate different person by color histogram have been proposed. People tracking is done by contrasting existing identity signature in the systems when an entrance event is detected in the ROI under a camera. Additional reduction on processing time of the whole system is achieved by taking into account of the adjacent relationship among areas. Special attention has been paid for system configurability such that the proposed software architecture can be easily adopted to different floor plan settings. We have conducted a continuing surveillance monitoring on the third floor of EE in NTUST, and the result shows moderate surveillance performance: 93% accuracy in entrance counting, and 76% accuracy in identification checking.

[1] S. Bahadori, L.Iocchi, G. R. Leone, F. Nardi, and L. Scozzafava, “Real-time location and tracking through fixed stereo vision,” Applied Intelligence, Vol.26, No.2, pp. 83-97, Nov. 2007.
[2] P. Bellavista, A. Corradi and L. Foschini,” Context-Aware Handoff Middleware for Transparent Service Continuity in Wireless Networks ,” Pervasive and Mobile Computing 3 (4), pp. 439-466, 2007.
[3] J. Cui, H. Zha, H. Zhao, and R. Shivasaki, “Multi-modal tracking of people using laser scanners and video camera, ” Image and Vision Computing, Vol.26, No.2, pp. 240-252, June 2007.
[4] A. K. Dey, D. Salber, G.D. Abowd and M. Futakawa,” Conference Assistant: Combining Context-Awareness with Wearable Computing,” International Symposium on Wearable Computers, Digest of Papers, pp. 21-28, 1999.
[5] A. Hampapur, R. Feris, and Y.L. Tian, “Capturing People in Surveillance Video,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, art. No. 4270509, 2007.
[6] I. Haritaglu, D. Harwood, and L. S. Davis, “W4：Real-time Surveillance of People and Their Activities,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 809-830, August 2000.
[7] J. W. Hsieh, Y. T. Hsu, H. Y. M. Liao, and C. C. Chen, "Video-based Human Movement Analysis and its Application to Surveillance Systems," IEEE Transactions on Multimedia, Vol. 10, No. 3, pp. 372-392, April 2008.
[8] S. Jabri, Z. Duric, and H. Wechsler, “Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information,” Proceedings - International Conference on Pattern Recognition, Vol.15, No.4 , pp. 627-630 , 2000.
[9] O. Javed, K. Shafique, and M. Shah, “A Hierarchical Approach to Robust Background Subtraction Using Color and Gradient Information,” Proceeding of Workshop on Motion Video Computing, pp. 22-27, Dec. 2002.
[10] J. Krumm, S. Harris, B. Meyers, B. Brumitt, M. Hale, and S. Shafer, “Multi-camera Multi-person Tracking for EasyLiving,” Proceedings of the 2000 Third IEEE International Workshop on Visual Surveillance, pp. 3-10, July 2000.
[11] M.S. Lee, “Detecting People in Cluttered Indoor Scenes,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, no. 2, pp. 804-809, Jun. 2000.
[12] S. Martin, C. Nugent, J. Wallace, G. Kernohan, B. McCreight and M. Mulvenna,” Using Context Awareness Within the 'Smart Home' Environment to Support Social Care for Adults with Dementia ,” Technology and Disability 19 (2-3), pp. 143-152, 2007.
[13] S. Pellegrini and L. Iocchi, “Human Posture Tracking and Classification through Stereo Vision and 3D Model Matching,” Eurasip Journal on Image and Video Processing, art. No. 476151, 2008.
[14] R. Pflugfelder and H. Bischof, “People Tracking Across two Distant Self-calibrated Cameras,” IEEE Conference on Advanced Video and Signal Based Surveillance, art. No. 4425343, pp. 393-398, 2007.
[15] S. Piva, M. Zara, G. Gera, and C.S. Regazzoni, “Color-based Video Stabilization for Real-time On-board Object Detection on High-speed Trains,” Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, pp.299-304, Jul. 2003.
[16] S. Phimoltares, C. Lursinsap, and K.Chamnongthai, “Face detection and facial feature localization without considering the appearance of image context,” Image and Vision Computing, Vol.25, No.5, pp.741-753, May 2007.
[17] A. Roy, S. K. Das, and K. Basu, “A predictive Framework for Location-aware Resource Management in Smart Homes,” IEEE Transactions on Mobile Computing, Vol. 6, No. 11, pp. 1270-1283, Nov. 2007.
[18] A. Talukder and L. Matthies, “Real-Time Detection of Moving Objects from Moving Vehicles Using Dense Stereo and Optical Flow,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 4, pp. 3718-3725, Oct. 2004.
[19] S. Velipasalar, Y. L. Tian, and A. Hampapur, “Automatic Counting of Interacting People by Using a Single Uncalibrated Camera,”in Proc. 2006 IEEE International Conference on Multimedia and Expo, pp. 1265-1268, 2006.
[20] P. Viola and M.J. Jones, “Robust Real-Time Face Detection,” International Journal of Computer Vision, Vol.57, No.2 , pp. 137-154, May 2004.
[21] S. Wachsmuth, S. Wrede and M. Hanheide, “Coordinating Interactive Vision Behaviors for Cognitive Assistance,” Computer Vision and Image Understanding 108 (1-2), pp. 135-149, 2007.
[22] N. Wanopoulos, N. Vasanthavada and R.L. Baker, “Design of an Image Edge Detection Filter Using the Sobel Operator,” IEEE Journal of Solid-State Circuits, Vol. 23, no. 2, pp. 358-367, Apr. 1988.
[23] HUNT HLC-83M Datasheet, www.hunt.com.tw/English/Downloads/UserManual/IPsecurity/HLC-83M_UserManual_Eng_V1.1.pdf
[24] M. Sonka, V. Hlavac, and R. Boyle, “Image Processing, Analysis, and Machine Vision,” 2nd Edition, PWS Publishing, 1998.
[25] M. Negnevitsky, “Artificial Intelligence: A Guide to Intelligent Systems,” Addison-Wesley, 2002.