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A grid-computing based multi-camera tracking system for vehicle plate recognition. (English) Zbl 1249.68273
Summary: There are several ways to implement a vehicle tracking system such as recognizing a vehicle’s color, shape or license plate. In this paper, we concentrate on recognizing a vehicle on a highway through license plate recognition. Generally, recognizing a license plate for a toll-gate system or parking system is easier than recognizing a license plate for a highway system. There are many cameras installed on a highway to capture images and every camera has different image angles. As a result, images are captured under various imaging conditions not focused on the vehicle itself. Therefore, we need a system that is able to recognize the object first. However, such a system consumes a large amount of time to complete the whole process. To overcome this drawback, we use grid computing. At the end of this paper, we discuss our results by considering an experiment.

MSC:
68T45 Machine vision and scene understanding
68U10 Computing methodologies for image processing
68U35 Computing methodologies for information systems (hypertext navigation, interfaces, decision support, etc.)
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References:
[1] Adshead H. G.: Optimising automatic tracking of multilayer boards. ACM SIGDA Newsletter 5 (1975), 3, 14-28 · doi:10.1145/1061425.1061428
[2] Alias M. A. B.: Pengesanan Kedudukan Nombor Plat Kereta Menggunakan Pendekatan Pekali Variasi. Bachelor Thesis, Universiti Teknologi Malaysia 1999
[3] Bagdanov A. D., Bimbo, A. del, Pernici F.: Explore multi-resolution views with PTZ and coordinated camera networks: Acquisition of high-resolution images through on-line saccade sequence planning. Proc. Third ACM Internat. Workshop on Video Surveillance & Sensor Networks VSSN’05. ACM Press, 2005, pp. 121-130
[4] Barroso J. A., Rafael A., Dagless E. L., Bulas-Cruz J.: Number plate reading using computer vision. Proc. Internat. Symposium on Industrial Electronics (ISIE-97), Guimaraes 1997, pp. 761-766
[5] Beymer D., McLauchlan P., Coihan, B., Malik J.: A real-time computer vision system for measuring traffic parameters. Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997, pp. 495-501
[6] Carranza J., Theobalt, Ch., Magnor M. A., Seidel H.-P.: Free-viewpoint video of human actors. ACM Trans. Graphics 22 (2003), 3, 569-577 · Zbl 05457193 · doi:10.1145/882262.882309
[7] Draghici S.: A neural network based artificial vision system for licence plate recognition. Internat. J. Neural Systems 8 (1997), 1, 113-126 · Zbl 05472084 · doi:10.1142/S0129065797000148
[8] Fan X., Xu D., Hou, J., Zheng G.: Reasoning about team tracking. ACM SIGSOFT Software Engineering Notes 23 (1998), 3, 79-82 · doi:10.1145/279437.279476
[9] Fidaleo D., Trivedi M.: Manifold analysis of facial gestures for face recognition. Proc. 2003 ACM SIGMM Workshop on Biometrics Methods and Applications, ACM Press 2003, pp. 65-69
[10] Gandhi T., Trivedi M. M.: Calibration of a reconfigurable array of omnidirectional cameras using a moving person. Proc. ACM 2nd International Workshop on Video Surveillance & Sensor Networks, ACM Press 2004, pp. 12-19
[11] Gatica-Perez D., Lathoud G., Odobez J.-M., McCowan I.: Recognizing communication patterns: Multimodal multispeaker probabilistic tracking in meetings. Proc. 7th International Conference on Multimodal Interfaces ICMI’05, ACM Press 2005, pp. 183-190
[12] Kilger M.: A shadow handler in a video-based realtime traffic monitoring system. Proc. IEEE Workhop on Applications of Computer Vision, 1992, pp. 11-18
[13] Koller D., Daniilidis, K., Nagel H. H.: Model-nased object tracking in monocular omage sequences of road traffic scenes. Internat. J. Computer Vision 10 (1993), 257-281 · doi:10.1007/BF01539538
[14] Krause A., Leskovec, J., Guestrin C.: Data association for topic intensity tracking. Proc. 23rd International Conference on Machine Learning ICML’06, ACM Press, 2006, pp. 497-504
[15] Kyo S., Koga T., Sakurai, K., Okazaki, Shin’ichiro: A robust vehicle detecting and tracking system for wet weather conditions using the IMAP-vision image processing board. Proc. Intelligent Transportation Systems, IEEE 99, 1999, pp. 423-428
[16] Cha B. Lee, E.: Fast and robust techniques for detection of car plate using HSV, weighted morphology. , 2002 · 164.125.165.168
[17] Lim B. L., Yeo W., Tan K. T., Teo C. Y.: A Novel DSP based real-time character classification and recognition algorithm for car plate detection and recognition. Proc. ICSP ’98 Fourth International Conference on Signal Processing IEEE, Beijing 1998, pp. 1269-1272
[18] Lin, Ch.-P., Tai, J.-Ch., Song K.-T.: Traffic monitoring based on real-time image tracking. Proc. Robotics and Automation, IEEE 03 (2003), pp. 2091-2096
[19] Martin F., Borges D.: Automatic car plate recognition using partial segmentation algorithm. Proc. Signal Processing, Pattern Recognition, and Applications 2003, Rhodes, 404-061
[20] Michele Z., Stefano, M., Carla M. M.: An efficient vehicle queue detection system based on image processing. Proc. 12th International Conference on Image Analysis and Processing (ICIAP’03), IEEE 03 (2003), pp. 232-237
[21] Mujica F. A., Leduc J.-P., Murenzi, R., Smith M. J. T.: A new motion parameter estimation algorithm based on the continuous wavelet transform. IEEE Trans. Image Processing (2000), 873-888 · Zbl 0970.94003 · doi:10.1109/83.841533
[22] Musa Z. B., Watada J.: A grid-computing based multi-camera tracking system for vehicle plate recognition. Proc. Czech-Japan Seminar, Kitakyushu 2006, pp. 184-189 · Zbl 1249.68273 · www.kybernetika.cz · eudml:33821
[23] Naor Z.: Tracking mobile users with uncertain parameters. Wireless Networks 9 (2003), 6, 637-646 · Zbl 02039184 · doi:10.1023/A:1025960502871
[24] Pitas I.: Digital Image Processing Algorithms. (Prentice Hall International Series in Acoustics, Speech and Signal Processing.) Prentice Hall, Englewood Cliffs, N.J. 1993 · Zbl 0782.68118
[25] Prati A., Vezzani R., Benini L., Farella, E., Zappi P.: Enlarge and enhance the view with video, audio and sensor networks: An integrated multi-modal sensor network for video surveillance. Proc. Third ACM International Workshop on Video Surveillance & Sensor Networks VSSN’05, ACM Press 2005, pp. 95-102
[26] Sang K. K., Dae W. K., Hang J. K.: A recognition of vehicle license plate using genetic algorithm based segmentation. Proc. Internat. Conference on Image Processing 1996, pp. 661-664
[27] Sato T.: Technical view: Situation recognition and its future in ubiquitous society - human support systems in terms of environmental system and contents system. Special Issue on Situation/Context Awareness Technologies for Human Support (T. Sato, J. Systems Control Inform. 49 (2005), 4
[28] Seto Y.: Trend of biometric security technology. Special Issue: Advances in Biometric Identification (Yoichi Seto, J. Soc. Instrum. Control Engrg. 43 (2004), 7
[29] Stefano R., Rodolfo Z.: A multiprocessor-oriented visual tracking system. IEEE Trans. Industrial Electronic 46 (1999), 4, 842-850 · doi:10.1109/41.778256
[30] Uchihashi S.: Video applications: Improvising camera control for capturing meeting activities using a floor plan. Proc. Ninth ACM International Conference on Multimedia, ACM Press, 2001, pp. 12-18
[31] Wang Y.-F., Chang E. Y., Cheng K. P.: Enlarge and enhance the view with video, audio and sensor networks: A video analysis framework for soft biometry security surveillance. Proc. Third ACM International Workshop on Video Surveillance & Sensor Networks VSSN’05, ACM Press, 2005
[32] Watada J., Musa Z. B.: A future view of a multi-camera tracking system. Proc. SICE-ICCAS 2006, Organized Session: SICE City, Busan 2006, pp. 71-78
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