| 摘要(英) |
With the advancement of computer technology and increasing needs of social security, studies of target object detection in aerial surveillance using image processing techniques are growing more and more important. These technologies can be employed in various applications, such as gathering enemy information for military purpose and searching for missing people in mountain areas. In intelligence transportation applications, aerial surveillance not only provides traffic monitoring but also assists traffic management, which can save a lot amount of human resources. Among the basic modules in intelligent aerial surveillance, vehicle detection plays a very important role.
In this thesis, we present an automatic vehicle detection system for aerial surveillance. First of all, background colors are eliminated and then features are extracted. In this system, we consider features including vehicle colors, edges and local feature points. For vehicle color extraction, we utilize color transform to separate vehicle colors and non-vehicle colors effectively. For edge detection, we apply moment-preserving method to adjust the thresholds for canny edge detector automatically, which increases the adaptability and accuracy for detection in various aerial images. Afterwards, a Dynamic Bayesian Network (DBN) is constructed for classification purpose. Based on the features extracted, a well trained DBN can estimate the probability of a pixel belonging to a vehicle or not.
In this work, features are obtained from a neighborhood region but the detection task is based on pixel-wise classification, which is more effective and efficient than multi-scale sliding window or region-based methods. Experiments were conducted on a variety of aerial videos and the average hit rate of testing videos is 92.04%. The number of false positives per frame of testing videos is 0.16616. The experimental results reveal that the proposed approach is feasible for generalization and effective for vehicle detection in various aerial videos.
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| 參考文獻 |
[1]S.M. Al-Garni and A. A. Abdennour, “Moving Vehicle Detection using Automatic Background Extractuib,” World Academy of Science, Engineering and Technology 24,2006
[2]R. Lin, X. Cao, Y. Xu, C. Wu, and H. Qiao, “Airborne moving vehicle detection for video surveillance of urban traffic,” Intelligent Vehicles Symposium, 2009
[3]L.W. Tsai, J.W. Hsieh, Member, IEEE, and K.C. Fan, Member, IEEE, “Vehicle detection using normalized color and edge map,” IEEE transactions on image processing, VOL. 16, NO. 3, 2007
[4]J.Y. Choi and Y.K. Yang, “Vehicle detection from aerial images using local shape information,” PSIVT 2009, LNCS 5414, pp.227-236, 2009
[5]H. Cheng and D. Butler, “Segmentation of aerial surveillance video using a mixture of experts,” IEEE Digital Imaging Computing: Techniques and Applications, 2005
[6]C. Tzomakas and W. Seelen, “Vehicle detection in traffic scenes using shadow,” Tech. Tep. 98-06 Inst. fur neuroinformatik, Ruhtuniversitat, Gemany, 1998
[7]C.G. Harris and M.J. Stephens, “A combined corner and edge detector,” Proc. 4th Alvey Veision Conf., Manchester, p.147-151, 1988
[8]L. Wang and Y. Shen, “An Algorithm Based On the Harris Corner Detector to Identify the Palmprint Extraction,” ICEMI, 2009
[9]J.F.Canny, “A Computational Approach to Edge Detection,” IEEE Trans Pattern Analysis and Machine Intelligence, 8(6):p.679-698, 1986
[10]C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, VOL. 20, NO. 3, p.273-297, 1995
[11]H. Simon, “Neural Networks: A Comprehensive Foundation,” 1998
[12]S. Rusell and P. Norvig, “Artificial Intelligence : A Modern Approach,” p.712-731
[13]N. Friedman, M. Goldszmidt, D. Heckerman, and S. Russell, “Challenge: Where is the impact of Bayesian network in learning?”, proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann Publishers, 1997
[14]Z. Ghahramani, “Learning Dynamic Bayesian Networks”
[15]C.K. Yang and W.H. Tsai, “Reduction of color space dimensionality by moment-perserving thresholding and its application for edge detection in color image,” Pattern Recognition Letters 17, 481-490, 1996
[16]I.P. Chen, M.Y. Lai and L.L. Wang, “An improvement in the moment-preserving thresholding method,” International Journal of Imaging Systems and Technology, vol. 18, p.365-370, 2008
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