移動物件的追蹤

移動物件追蹤的實驗如圖4.18 與 4.19 所示，圖中移動物體後方的粉紅線表示我們 追蹤的軌跡線，劃線的依據是從CF 開始與前八張影像的重心連接，

圖4.18 高速公路側面影像的車輛追蹤

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圖4.19 新竹市經國路與西大路口的車輛追蹤

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第五章 結論與未來展望

在本研究中，我們提出了針對背景擷取的方法輔助以機率的選取模式，來有效並快 速的獲得背景影像。獲得了背景影像後，我們再藉由背景影像與前景影像的差異性，來 獲得最主要的移動物件，在此，我們亦提出了適應性門檻值的選取方式，來用以對於各 種不同的道路影像，都能輔助先前得到的背景影像，來有效地切割出移動物件。獲得物 件之後，還會透過陰影去除的方式來避免移動物件之間，因為陰影問題而造成連接。我 們的背景更新方式，能針對長時環境來不斷更新我們初始收斂的背景。在實驗結果方 面，都能獲得良好的結果。

本研究的方法，未來有許多應用的空間，因為我們能有效的獲得移動物件主要的參 數，在交通監控上面，可以應用在意外偵測，防撞功能等，搭配環境參數還能有效的偵 測車速。此外，在監控系統上，一般還會搭配網路傳輸的功能，可以讓使用者隨時透過 網路來觀看所要監控的場所，而要透過網路來傳送大量的影像，除了較大的網路頻寬之 外，好的壓縮技術更是在實現網路傳輸上面，更為重要的腳色。由於我們是將影像分成 了前景與背景的部分，那麼我們便可以只要傳送前景的部分給使用者端，如此便能大大 的降低資料量，更加提升了壓縮的效能，使的監控系統能更具有實用性。

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參考文獻

[1] O. Masoud, N. P. Papanikolopoulos, and E. Kwon, “Vision-based monitoring of weaving sections,” Proc. of IEEE Conference on Intelligent Transportation　 Systems, pp. 770-775, October 1999.

[2] O. Masoud, N. P. Papanikolopoulos, and E. Kwon, “The use of computer vision in monitoring weaving sections,” IEEE Transactions on Intelligent Transportation Systems, vol. 2, no. 1, pp. 18-25, 2001.

[3] C. E. Smith, C. A. Richards, S. A. Brandt, and N. P. Papanikolopoulos, “Visual tracking for intelligent vehicle-highway systems,” IEEE Transactions on Vehicular Technology, vol. 45, no. 4, pp. 744-759, November 1996.

[4] C. Li, K. Ikeuchi, and M. Sakauchi, “Acquisition of traffic information using a video camera with 2D spatio-temporal image transformation technique,” Proc. of IEEE Conference on Intelligent Transportation Systems, pp. 634-637, October 1999.

[5] R, Defauw, S. Lakshmanan, and K. V. Prasas, “A system for small target detection, tracking, and classification,” Proc. of IEEE Conference on Intelligent Transportation Systems, pp. 639-648, October 1999.

[6] D. Gao, and J. Zhou, “Adaptive background estimation for real-time traffic monitoring,”

Proc. of IEEE Conference on Intelligent Transportation Systems, pp. 330-333, August 2001.

[7] D. W. Lim, S. H. Choi and J. S. Jun, “Automated Detection of all Kinds of Violations at a Street Intersection Using Real-Time Individual Vehicle Tracking,” Proc. of Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 126-129, 2002.

[8] D. J. Dailey, L. Li, “An algorithm to estimate vehicle speed using uncalibrated cameras,”

Proc. of IEEE/IEEJ/JSAI International Conference on Intelligent Transportation System, pp. 441-446, 1999.

[9]T. Nakanishi, K. Ishii, “Automatic vehicle image extraction based on spatio-temporal image analysis,” Proc. Of 11th IAPR International Conference on Pattern Recognition, Vol. 1, Conference A: Computer Vision and Applications, pp. 500-504, 1992.

[10]M. Fathy and M. Y. Siyal, “An Image Detection Technique Based on Morphological Edge Detection and Background Differencing for Real-Time Traffic Analysis,” Pattern Recognition, vol. 16, pp. 1321-1330, 1995.

[11] A. H. S. Lai and N. H. C. Yung, “A Fast and Accurate Scoreboard Algorithm for Estimating Stationary Background in an Image Sequence,” Proc. of IEEE Int’l Symp. on Circuits and Systems, vol. 4, pp. 241-244, 1998.

[12]A. J. Lipton, H. Fujiyoshi and R. S. Patil, “Moving Target Classification and Tracking from Real-Time Video,” Proc. of Fourth IEEE Workshop on Application of Computer Vision, pp. 8-4, 1998.

48

[13] W. Long and Y. H. Yang, “Stationary Background Generation: an Alternative to the Difference of Two Images,” Pattern Recognition, vol 23, no. 12, pp. 1351-1359, 1990.

[14] R. Cucchiara, M. Piccardi and P. Mello, “Image Analysis and Rule-Based 7-2 Reasoning for a Traffic Monitoring System,” IEEE Trans. on Intelligent Transportation System, vol.

1, no. 2, pp. 119-130, June 2000.

[15]B. Coifman, and M. Cassidy, “Vehicle reidentification and travel time measurement, part I: congested freeways,” Proc. of IEEE Conference on Intelligent Transportation Systems, pp. 138-143, August 2001.

[16]D. Koller, J. Daniilidis and H. H. Nagel, “Model-Based Object Tracking in Monocular Image Sequences of Road Traffic Scenes,” Int’l J. Computer Vision, vol. 10, pp. 257-281, 1993.

[17] J.M. Scanlan, D.M. Chabries, and R.W. Christiansen, “A Shadow Detection and Removal Algorithm for 2-D Images,” Proc. Int’l Conf. Acoustics, Speech and Signal Processing, vol. 4, pp. 2057-2060, 1990.

[18] I. Mikic, P.C. Cosman, G.T. Kogut, and M.M. Trivedi, “Moving Shadow and Object Detection in Traffic Scenes,” Proc. Int’l Conf. Pattern Recognition, vol. 1, no. 1, pp.

321-324, 1998.

[19] Mohanna, F., Mokhtarian, F., “Improved curvature estimation for accurate localisation of active contours”, Proceeding of International Conference on Image Processing, vol. 2, pp.

781-784 Oct. 2001.

[20] Y. Mae, Y. Shirai, J. Miura, and Y. Kuno, “Object Tracking in Cluttered Background Based on Optical Flow and Edges,” Proc. Of 13th Int’l Conf. On Pattern Recognition, vol.

1,pp. 196-200, 1996.

[21] R. Cucchiara, C. Grana, M. Piccardi, A. Prati, and S. Sirotti, “Improving Shadow Suppression in Moving Object Detection with HSV Color Information,” Proc. IEEE Intelligent Transportation Systems Conf., pp. 334-339, Aug. 2001.

[22] T. Horprasert, D. Harwood, and L.S. Davis, “A Statistical Approach for Real-Time Robust Background Subtraction and Shadow Detection,” Proc. IEEE Int’l Conf.

Computer Vision, Frame Rate Workshop, pp. 1-19, 1999.

[23]Utsumi, A., Mori, H., Ohya, J., Yachida, M., “Multiple-human tracking using multiple cameras,”

Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 498 – 503, April 1998.

[24] M. Haag and H. Nagel, “Combination of edge element and optical flow estimates for 3D-model-based vehicle tracking in traffic image sequences,” International Journal of Computer Vision, vol. 35, no. 3, pp. 295-319, 1999.

[25]S. Das and B. Bhanu, “A System for Model-Based Object Recognition in Perspective Aerial Images,” Pattern Recognition, vol. 3, no. 34, pp. 465-491, 1998.

[37]J. Stauder, R. Mech, and J. Ostermann, “Detection of Moving Cast Shadows for Object Segmentation,” IEEE Trans. Multimedia, vol. 1, no. 1, pp. 65-76, Mar. 1999.

49

[27]G. Welch, G. Bishop, “An Introduction to the Kalman Filter,” 2001.

[28]M. Nadler, E. P. Smith, Pattern Recognition Engineering, A Wiley-Interscience Publication, 1993.

[29]W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, Numerical Recipes in C – The Art of Scientific Computing, Cambridge University Press, 1992.

[30]T. Naito, ”Robust recognition methods for inclined license plates under various illumination conditions outdoors,” Proc. of IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp. 697–702,1999.

[31]C. Busch, R. Domer, C. Freytag, and H. Ziegler, “Feature based recognition of traffic video streams for online route tracing,” Proc. of IEEE Conference on Vehicular Technology, vol. 3, pp.1790-1794, 1998.

[32]R. Zunino and S. Rovetta, “Vector quantization for license-plate location and image coding,” IEEE Transactions on Industrial Electronics, vol. 47, pp.159–167, Feb. 2000.

[34]M. Shridhar, Miller, G. Houle, and L. Bijnagte, “Recognition of license plate images:

issues and perspectives,” Proc. of the Fifth International Conference on Document Analysis and Recognition, p.17–20, 1999.

[35]K. K. Kim, K. I. Kim, J. B. Kim, and H. J. Kim, “Learning-based approach for license plate recognition,” Proc. of IEEE Workshop on Neural Networks for Signal Processing, vol. 2, pp.614–623, 2000.

[36]H.A. Hegt, R.J. Haye, and N.A. Khan, “A high performance license plate recognition system,” Proc. of IEEE International Conference on Systems, Man, and Cybernetics, pp.

4357–4362, 1999.