Research on the detection and recognition system of target vehicles based on fusion algorithm
Vol 2, Issue 2, 2024
Issue release: 15 November 2024
VIEWS - 538 (Abstract)
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Abstract
In modern society, the monitoring of vehicles is paramount for upholding traffic safety and order. This paper delves into a Graphical User Interface (GUI)-driven system for vehicle target detection and integration. This system seamlessly integrates three pivotal functionalities: vehicle type recognition, license plate recognition, and driver face recognition. It automates the vehicle inspection assessment process through an intuitive user interface. Specifically, for license plate recognition, the system leverages traditional computer vision techniques, including color and grayscale processing, radon transform, morphological operations, edge detection, character segmentation, and character recognition. The driver face recognition module incorporates methods such as image gray scaling, Gaussian filtering for denoising, face detection, and feature extraction. Meanwhile, vehicle type recognition is achieved by extracting Histogram of Oriented Gradients (HOG) features and utilizing a Support Vector Machine (SVM) classifier. Experimental findings highlight the system’s remarkable recognition accuracy, offering a highly efficient and dependable solution for the automated inspections of vehicles.
Keywords
References
1. Jiang J, Li Y, Li Y, et al. Smart transportation systems using learning method for urban mobility and management in modern cities. Sustainable Cities and Society. 2024; 108105428.
2. Soumyajit G, Sourajit M, Kumar P S, et al. Two decades of vehicle make and model recognition—Survey, challenges and future directions. Journal of King Saud University Computer and Information Sciences. 2024; 36(1): 101885.
3. Tang M. Research on Image Preprocessing Algorithm of License Plate Recognition System. Advances in Computer, Signals and Systems. 2023; 7(11): 378-402.
4. Saadi I, Cunningham DW, Abdelmalik T, et al. Driver’s facial expression recognition: A comprehensive survey. Expert Systems with Applications. 2024; 242122784.
5. Saha S. A Review on Automatic License Plate Recognition System. CoRR. 2019; abs/1902.09385.
6. Hou X, Fu M, Wu X, et al. Vehicle License Plate Recognition System Based on Deep Learning Deployed to PYNQ, In: Proceedings of 2018 18th International Symposium on Communications and Information Technologies (ISCIT); 26-29 September 2018; Bangkok, Thailand.
7. Bledsoe W. Man-machinefacial recognition. Panoramie Research Inc. Palo AITO, CA; 1966. p. 22.
8. Berto R, Poggio T. Face recognition: Feature versus templates. IEEE Trans. 1993; 15(10): 1042-1052.
9. Hong Z. Algebraic feature extraction of image for recognition. Pattern Recognition. 1991; 24(3): 211-219.
10. Nakamura O, Mathur S, Minami T. Identification of human faces based on isodensity maps. Pattern Recognition. 1991; 24(3): 263–272.
11. Lades M, Vorbuggen J, Buhmann J, et al. Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. on Computers. 1991; 42(3): 300-311.
12. Samaria F, Young S. HMM-based architecture for face identification. Image and Vision Computing. 1994; 12(8).
13. Kin-Man L, Hong Y. An analytic-to-holistic approach for face recognition based on a single frontal view. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998; 20(7): 673-686.
14. Lanitis A, Taylor CJ, Cootes TF. Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1997; 19(7): 743-756.
15. Canny JF. A theory of edge detection. IEEE Trans Pattern Anal Mach Intell. 1986; 8: 147-163.
16. Wu T, Wang L, Zhu J. Image Edge Detection Based on Sobel with Morphology. In: Proceedings of 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC); 15-17 October 2021; Xi’an, China.
17. Premnath SP, Gowr PS, Ananth JP, et al. Image enhancement and blur pixel identification with optimization-enabled deep learning for image restoration. Signal, Image and Video Processing. 2024; 18(5): 4525-4540.
18. Zhang X, Han B. Development of Car Security Information Management System Based on SQL Server 2000 Database Technology. Forest Engineering. 2008; (02): 92-93.
19. Chen P, Cao W, Zhang H. Research on Coarse License Plate Localization Algorithm in Complex Backgrounds. Computer Engineering and Applications. 2009; 45(17): 228-230+234.
20. Liu Z, Wang G. Research on Lane Line Recognition Based on Traditional Edge Operators (Chinese). Modern Electronics Technique. 2024; 47(07): 61-65. doi: 10.16652/j.issn.1004-373x.2024.07.010
21. Rui X. Application of Radon Transform in Skew Correction of License Plates. China Science and Technology Information. 2009; (12): 146-147.
22. José G, Paola M, Matias V, et al. A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems. Computational intelligence and neuroscience. 2019; 20193238574.
23. Sharma N, Dahiya KP, Marwah RB. Performance comparison of various techniques for automatic licence-plate recognition systems. International Journal of Cloud Computing. 2022; 11(2): 138-156.
24. Sun Y, Yu H, Jia S, et al. A Character Defect Detection Algorithm Based on Edge Shape Features. Computer Applications and Software. 2023; 40(09): 177-183.
25. Pan W, Yang Y, Li H. A Digital Image Denoising Algorithm Based on Gaussian Filtering and Bilateral Filtering. ITM Web of Conferences. 2018; 1701006-01006.
26. Maale RB, Nandyal SD. Face Recognition Based on Haar Cascade Classifier. Journal of Research in Science and Engineering. 2021; 3(5): 236-254.
27. Liu Z, Wu X, Ni J, et al. Driver Intention Recognition Based on HMM and SVM Cascade Algorithm. Automotive Engineering. 2018; 40(07): 858-864.
28. Jin C, Cui Y, Wang Y. Application of Particle Swarm Optimization SVM Algorithm in Gas Analysis. Journal of Electronic Measurement and Instrumentation. 2012; 26(07): 635-639.
29. Anil J, Padma LS. A novel fast hybrid face recognition approach using convolutional Kernel extreme learning machine with HOG feature extractor. Measurement: Sensors. 2023; 30: 1076-1095.
30. Qingtian G, Haoyu Z, Fanhua Y, et al. Algorithm for Vehicle Type Recognition Based on Improved HOG Feature Extraction. Chinese Optics. 2018; 11(02): 174-181.
31. Xiao H, Dong H, Sang E, et al. Artificial Intelligence-Oriented User Interface Design and Human Behavior Recognition based on Human-Computer Nature Interaction. International Journal of Humanoid Robotics. 2023; 20(06): 203-233.
32. Chen L, Cui L, Huang R, et al. Bio-inspired neural network with application to license plate recognition: hysteretic ELM approach. Assembly Automation. 2016; 36(2): 172-178.
33. Chen S, ZhouY, Fang Y. Car Model Recognition Based on Convolutional Neural Network. Computer Applications and Software. 2017; 34(11): 228-231.
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Copyright (c) 2024 Wenlei Wang, Chongfei Huai, Lingyin Meng, Ziming Wang, Hao Zhang
License URL: https://creativecommons.org/licenses/by/4.0/
Editor-in-Chief
Prof. Youssri Hassan Youssri
Cairo University, Egypt
Processing Speed (2023)
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- ＜7 days: submission to screening review decision
- 53 days: received to accepted
- 68 days: received to online
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