Previous |  Up |  Next


vehicle plate recognition; grid computing; recognition system; tracking system
There are several ways that can be implemented in a vehicle tracking system such as recognizing a vehicle color, a shape or a vehicle plate itself. In this paper, we will concentrate ourselves on recognizing a vehicle on a highway through vehicle plate recognition. Generally, recognizing a vehicle plate for a toll-gate system or parking system is easier than recognizing a car plate for the highway system. There are many cameras installed on the highway to capture images and every camera has different angles of images. As a result, the images are captured under varied imaging conditions and not focusing 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 installed this process with grid computing as a solution. At the end of this paper, we will discuss our obtained result from an experiment.
[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. In: 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. In: 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. In: 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 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 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. In: 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. In: 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. In: 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. In: 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. In: 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. In: 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
[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. In: 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. In: Proc. Robotics and Automation, IEEE 03 (2003), pp. 2091–2096
[19] Martin F., Borges D.: Automatic car plate recognition using partial segmentation algorithm. In: 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. In: 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 DOI 10.1109/83.841533 | Zbl 0970.94003
[22] Musa Z. B., Watada J.: A grid-computing based multi-camera tracking system for vehicle plate recognition. In: Proc. Czech–Japan Seminar, Kitakyushu 2006, pp. 184–189
[23] Naor Z.: Tracking mobile users with uncertain parameters. Wireless Networks 9 (2003), 6, 637–646 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 MR 1272249 | 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. In: 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. In: 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. In: Special Issue on Situation/Context Awareness Technologies for Human Support (T. Sato, ed.), J. Systems Control Inform. 49 (2005), 4
[28] Seto Y.: Trend of biometric security technology. In: Special Issue: Advances in Biometric Identification (Yoichi Seto, ed.), 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. In: 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. In: 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. In: Proc. SICE-ICCAS 2006, Organized Session: SICE City, Busan 2006, pp. 71–78
Partner of
EuDML logo