Proposing Real-time Parking System for Smart Cities using Two Cameras
الموضوعات :
Phat Nguyen Huu
1
,
Loc Hoang Bao
2
1 - School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam
2 - School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam
الکلمات المفتاحية: Object Detection, Single Shot Detector, Multi-View Cameras, Automatic License Plate Recognition, ALPR,
ملخص المقالة :
Today, cars are becoming a popular means of life. This rapid development has resulted in an increasing demand for private parking. Therefore, finding a parking space in urban areas is extremely difficult for drivers. Another serious problem is that parking on the roadway has serious consequences like traffic congestion. As a result, various solutions are proposed to solve basic functions such as detecting a space or determining the position of the parking to orient the driver. In this paper, we propose a system that not only detects the space but also identifies the vehicle's identity based on their respective license plate. Our proposal system includes two cameras with two independent functions, Skyeye and LPR cameras, respectively. Skyeye module has function to detect and track vehicles while automatic license plate recognition system (ALPR) module detects and identifies license plates. Therefore, the system not only helps drivers to find suitable parking space but also manages and controls vehicles effectively for street parking. Besides, it is possible to detect offending vehicles parking on the roadway based on its identity. We also collect a set of data that correctly distributes for the context in order to increase the system's performance. The accuracy of proposal system is 99.48% that shows the feasibility of applying into real environments.
[1] Z. Pala and N. Inanc, “Smart Parking Applications Using RFID Technology,” 2007 1st Annual RFID Eurasia, 2007, pp. 1-3.
[2] J. P. Benson et al., “Car-Park Management using Wireless Sensor Networks,” Proceedings. 2006 31st IEEE Conference on Local Computer Networks, 2006, pp. 588-595.
[3] A. Kianpisheh, N. Mustaffa, P. Limtrairut, and P. Keikhosrokiani, “Smart Parking System (SPS) architecture using ultrasonic detector,” Int. J. Softw. Eng. its Appl., vol. 6, no. 3, pp. 51–58, 2012.
[4] G. Ostojic, S. Stankovski, M. Lazarevic and V. Jovanovic, “Implementation of RFID Technology in Parking Lot Access Control System,” 2007 1st Annual RFID Eurasia, 2007, pp. 1-5.
[5] J. Wolff, T. Heuer, Haibin Gao, M. Weinmann, S. Voit and U. Hartmann, “Parking monitor system based on magnetic field senso,” 2006 IEEE Intelligent Transportation Systems Conference, 2006, pp. 1275-1279.
[6] N. H. H. Mohamad Hanif, Mohd Hafiz Badiozaman and H. Daud, “Smart parking reservation system using short message services (SMS),” 2010 International Conference on Intelligent and Advanced Systems, 2010, pp. 1-5.
[7] A. Sayeeraman and P. S. Ramesh, “Zigbee and gsm based secure vehicle parking management and reservation system,” J. Theor. Appl. Inf. Technol., vol. 37, no. 2, pp. 199–203, 2012.
[8] H. Ichihashi, T. Katada, M. Fujiyoshi, A. Notsu and K. Honda, “Improvement in the performance of camera based vehicle detector for parking lot,” International Conference on Fuzzy Systems, 2010, pp. 1-7.
[9] R. Yusnita, F. Norbaya, and N. Basharuddin. “Intelligent parking space detection system based on image processing,” International Journal of Innovation, Management and Technology, vol. 3, no. 3, pp. 232-235, 2012.
[10] S. Banerjee, P. Choudekar and M. K. Muju, "Real time car parking system using image processing," 2011 3rd International Conference on Electronics Computer Technology, 2011, pp. 99-103.
[11] S. Khaled, and H. Tounsi, “Parking space detection system using video images,” Transportation Research Record: J. of the Transportation Research Board 2537, no. 1, pp. 137-147, 2015.
[12] S. H. Pratap, O. P. Uniyal, and K. Joshi, “An approach to implement cost efficient space detection technology with lower complexity for smart parking system,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 15, no. 3, pp. 415-419, 2015.
[13] S. Saleh Al-Amri, N. V. Kalyankar, and Khamitkar S, “Image segmentation by using thershold techniques,” Journal of Computing. vol. 2, no. 5, 2010.
[14] S. Li, H. Dawood and P. Guo, “Comparison of linear dimensionality reduction methods in image annotation,” 2015 Seventh International Conference on Advanced Computational Intelligence (ICACI), 2015, pp. 355-360.
[15] R. Mehmood, R. Bie, H. Dawood and H. Ahmad, "Fuzzy clustering by fast search and find of density peaks," 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI), 2015, pp. 258-261.
[16] A. R. Pathak, M. Pandey,S. Rautaray, “Application of deep learning for object detection,” Proceding Comput. Sci, vol. 132, pp. 1706–1717, 2018.
[17] X. Wang, “Deep learning in object recognition, detection, and segmentation,” Trends Signal Process, vol. 8, pp. 217–382, 2016.
[18] M. Shafiee, B. Chywl, F. Li, A. Wong, “Fast YOLO: A fast you only look once system for real-time embedded object detection in video,” arXiv:1709.05943, 2017.
[19] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You only look once: unified, real-time object detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779-788.
[20] R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580-587.
[21] S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.
[22] A. Mauri, R. Khemmar, B. Decoux, N. Ragot, R. Rossi, R. Trabelsi, R. Boutteau, R. Ertaud, J. Savatier, “Deep learning for real-time 3D multi-object detection, localisation, and tracking: application to smart mobility,” Sensors, vol. 20, no. 2, pp. 1-15, 2020.
[23] J. Sang, Z. Wu, P. Guo P, et al, “An improved YOLOv2 for vehicle detection,” Sensors (Basel), vol 18, no. 12, pp. 1-15, 2018.
[24] J. C. Nascimento, A. J. Abrantes and J. S. Marques, “An algorithm for centroid-based tracking of moving objects,” 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999, pp. 3305-3308, vol.6.
[25] M. Everingham, L. V. Gool, C.K.I. Williams, J. Winn, A. Zisserman, “The pascal visual object classes (voc) challenge,” International J. of Computer Vision, vol. 88, no. 2, pp. 303–338, 2010.
[26] G. R. Gonalves, S.P.G. Silva,D. Menotti, W.R. Schwartz, “Benchmark for license plate character segmentation,” J. of Electronic Imaging, vol. 25, no. 5, pp. 1–5, 2016.
[27] S. M. Silva and C.R. Jung, “License plate detection and recognition in unconstrained scenarios,” Lecture Notes in Computer Science, vol 11216, Springer, Cham, pp. 593–609, 2018.
[28] P.N. Huu, L.H. Bao, and H.L. The, “Proposing an image enhancement algorithm using CNN for applications of face recognition system,” J. Adv. Math. Comput. Sci., vol. 34, no. 6, pp. 1–14, 2020.
[29] Computer Vision, “Aggregate data”, Online: https://thigiacmaytinh.com/tai-nguyen-xu-ly-anh/tong-hop-data-xu-ly-anh/ (accessed Jun. 30, 2020).
[30] T.Y. Lin, et al, “Microsoft COCO: Common objects in context,” in ECCV 2014: Computer Vision – ECCV 2014, vol. 8693, pp. 740-755, 2014.
[31] K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778.
[32] W. Liu, et al., “SSD: Single shot multiBox detector,” In ECCV 2016: Computer Vision – ECCV 2016, vol. 9905, pp. 21-37, 2016.
[33] J. Redmon and A. Farhadi, “YOLO9000: Better, faster, stronger,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6517-6525.
[34] A. G. Howard, et. al, “Mobilenets: efficient convolutional neural networks for mobile vision applications,” arXiv:1704.04861, pp. 1-9, 2017.
[35] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510-4520.
[36] D. Erhan, C. Szegedy, A. Toshev and D. Anguelov, “Scalable object detection using deep neural networks,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2155-2162.
[37] C. Szegedy, S. Reed, D. Erhan, D. Anguelov, “Scalable, high-quality object detection,” arXiv:1412.1441 v3, pp. 1-10, 2015.
[38] R. Girshick, “Fast R-CNN,” 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440-1448.
[39] M. Abadi, et. al, “TensorFlow: a system for large-scale machine learning,” In Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation (OSDI'16). USENIX Association, USA, pp. 265–283, 2016.
