A Comprehensive Study of Real-Time Vacant Parking Space Detection Towards the need of a Robust Model

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Rifath Mahmud
A. F. M. Saifuddin Saif
Dipta Gomes


Detection of vacant parking space is becoming a challenging task gradually. Space utilization and management of vehicle space is now a demandable field of research. Searching for an empty parking space in congested traffic is a time-consuming process. The existing vacant parking space detection methods are not robust or generalized for images captured from different camera viewpoints. Finding a proper parking space in a busy city is really a challenging issue and people are facing this problem on a daily basis. The main purpose of this research is to comprehensively discuss the previous researches of vacant parking space detection and compare them from different aspects. Methods used in previous researches are descriptively discussed along with their advantages and disadvantages. The frameworks of previous researches were compared on six generalized phases and the experimental results are compared in terms of dataset, accuracy, processing time and other performance measures.  This research also focuses on the challenges of vision-based vacant parking space detection which will contribute to future researches and researchers can work to overcome these challenges.

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How to Cite
Mahmud, R., Saif, A. F. M. S., & Gomes, D. (2020). A Comprehensive Study of Real-Time Vacant Parking Space Detection Towards the need of a Robust Model. AIUB Journal of Science and Engineering (AJSE), 19(3), 99 - 106. Retrieved from http://ajse.aiub.edu/index.php/ajse/article/view/80


[1] N. Bibi, M. N. Majid, H. Dawood, and P. Guo, “Automatic Parking Space Detection System,” Proc. - 2017 2nd Int. Conf. Multimed. Image Process. ICMIP 2017, vol. 2017-Janua, no. October, pp. 11–15, 2017.
[2] R. M. Nieto, A. Garcia-Martin, A. G. Hauptmann, and J. M. Martinez, “Automatic Vacant Parking Places Management System Using Multicamera Vehicle Detection,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 3, pp. 1069–1080, 2019.
[3] D. Di Mauro, A. Furnari, G. Patanè, S. Battiato, and G. M. Farinella, “Estimating the occupancy status of parking areas by counting cars and non-empty stalls,” J. Vis. Commun. Image Represent., vol. 62, pp. 234–244, 2019.
[4] F. Dornaika, K. Hammoudi, M. Melkemi, and T. D. A. Phan, “An efficient pyramid multi-level image descriptor: application to image-based parking lot monitoring,” Signal, Image Video Process., 2019.
[5] S. Nurullayev, S. W. Lee, D. Convolutional, and N. Network, “Generalized parking occupancy analysis based on dilated convolutional neural network,” Sensors (Switzerland), vol. 19, no. 2, 2019.
[6] H. Bura, N. Lin, N. Kumar, S. Malekar, S. Nagaraj, and K. Liu, “An edge based smart parking solution using camera networks and deep learning,” Proc. - 2018 IEEE Int. Conf. Cogn. Comput. ICCC 2018 - Part 2018 IEEE World Congr. Serv., pp. 17–24, 2018.
[7] R. M. Nieto, Á. García-martín, A. G. Hauptmann, and J. M. Martínez, “System Using Multicamera Vehicle Detection,” pp. 1–12, 2018.
[8] H. T. Vu and C. C. Huang, “Parking Space Status Inference Upon a Deep CNN and Multi-Task Contrastive Network with Spatial Transform,” IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 4, pp. 1194–1208, 2019.
[9] M. R. Lee and D. T. Lin, “Vehicle counting based on a stereo vision depth maps for parking management,” Multimed. Tools Appl., vol. 78, no. 6, pp. 6827–6846, 2019.
[10] J. Martinez Garcia, D. Zoeke, M. Vossiek, J. M. García, S. Member, and D. Zoeke, “MIMO-FMCW radar-based parking monitoring application with a modified convolutional neural network with spatial priors,” IEEE Access, vol. 6, pp. 41391–41398, 2018.
[11] I. No and G. N. Sarage, “Study of Various Noise Removal Techniques,” vol. 6, no. 1, pp. 174–177, 2015.
[12] C. Jang and M. Sunwoo, “Semantic segmentation-based parking space detection with standalone around view monitoring system,” Mach. Vis. Appl., vol. 30, no. 2, pp. 309–319, 2019.
[13] G. Amato, F. Carrara, F. Falchi, C. Gennaro, C. Vairo, and G. Moruzzi, “Car parking occupancy detection using smart camera networks and Deep Learning,” Proc. - IEEE Symp. Comput. Commun., vol. 2016-Augus, no. Dl, pp. 1212–1217, 2016.
[14] T. H. P. Jensen, H. T. Schmidt, N. D. Bodin, K. Nasrollahi, and T. B. Moeslund, “Parking Space Occupancy Verification - Improving Robustness using a Convolutional Neural Network,” no. Visigrapp, pp. 311–318, 2017.
[15] S. Valipour, M. Siam, E. Stroulia, and M. Jagersand, “Parking-stall vacancy indicator system, based on deep convolutional neural networks,” 2016 IEEE 3rd World Forum Internet Things, WF-IoT 2016, pp. 655–660, 2017.
[16] L. Baroffio, L. Bondi, M. Cesana, A. E. Redondi, and M. Tagliasacchi, “A visual sensor network for parking lot occupancy detection in Smart Cities,” IEEE World Forum Internet Things, WF-IoT 2015 - Proc., pp. 745–750, 2015.
[17] P. R. L. De Almeida, L. S. Oliveira, A. S. Britto, E. J. Silva, and A. L. Koerich, “PKLot-A robust dataset for parking lot classification,” Expert Syst. Appl., vol. 42, no. 11, pp. 4937–4949, 2015.
[18] G. Amato, F. Carrara, F. Falchi, C. Gennaro, C. Meghini, and C. Vairo, “Deep learning for decentralized parking lot occupancy detection,” Expert Syst. Appl., vol. 72, pp. 327–334, 2017.
[19] Q. Li, C. Lin, and Y. Zhao, “Geometric features-based parking slot detection,” Sensors (Switzerland), vol. 18, no. 9, 2018.
[20] Q. Wu, C. Huang, S. Wang, W. Chiu, and T. Chen, “Robust Parking Space Detection Considering Inter-Space Correlation,” pp. 659–662, 2007.
[21] J. M. Menéndez, C. G. del Postigo, and J. Torres, “Vacant parking area estimation through background subtraction and transience map analysis,” IET Intell. Transp. Syst., vol. 9, no. 9, pp. 835–841, 2015.
[22] C. C. Huang and H. T. Vu, “Vacant Parking Space Detection Based on a Multilayer Inference Framework,” IEEE Trans. Circuits Syst. Video Technol., vol. 27, no. 9, pp. 2041–2054, 2017.
[23] M. A. Alimi, I. Masmoudi, A. Jamoussi, and A. Wali, “Trajectory analysis for parking lot vacancy detection system,” IET Intell. Transp. Syst., vol. 10, no. 7, pp. 461–468, 2016.
[24] L. Li, C. Li, Q. Zhang, T. Guo, and Z. Miao, “Automatic parking slot detection based on around view monitor (AVM) systems,” 2017 9th Int. Conf. Wirel. Commun. Signal Process. WCSP 2017 - Proc., vol. 2017-Janua, pp. 1–6, 2017.
[25] M. Ahrnbom, K. Astrom, and M. Nilsson, “Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., pp. 1609–1615, 2016.
[26] M. Tschentscher, C. Koch, M. König, J. Salmen, and M. Schlipsing, “Scalable real-time parking lot classification: An evaluation of image features and supervised learning algorithms,” Proc. Int. Jt. Conf. Neural Networks, vol. 2015-Septe, 2015.
[27] L. Zhang, J. Huang, X. Li, and L. Xiong, “Vision-Based Parking-Slot Detection: A DCNN-Based Approach and a Large-Scale Benchmark Dataset,” IEEE Trans. Image Process., vol. 27, no. 11, pp. 5350–5364, 2018.
[28] S. E. Shih and W. H. Tsai, “A convenient vision-based system for automatic detection of parking spaces in indoor parking lots using wide-angle cameras,” IEEE Trans. Veh. Technol., vol. 63, no. 6, pp. 2521–2532, 2014.
[29] D. Di Mauro, M. Moltisanti, G. Patane, S. Battiato, and G. M. Farinella, “Park Smart,” 2017 14th IEEE Int. Conf. Adv. Video Signal Based Surveillance, AVSS 2017, 2017.
[30] C. Brown, “Using Computer Vision Techniques for Parking Space Detection in Aerial Imagery,” Adv. Comput. Vis., pp. 190–204, 2014.
[31] P. Jain and V. Tyagi, “A survey of edge-preserving image denoising methods,” 2014.
[32] X. Ling, J. Sheng, O. Baiocchi, X. Liu, and M. E. Tolentino, “Identifying parking spaces & detecting occupancy using vision-based IoT devices,” GIoTS 2017 - Glob. Internet Things Summit, Proc., 2017.
[33] C. C. Huang, Y. S. Tai, and S. J. Wang, “Vacant parking space detection based on plane-based bayesian hierarchical framework,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 9, pp. 1598–1610, 2013.
[34] X. Xiang, N. Lv, M. Zhai, and A. El Saddik, “Real-Time Parking Occupancy Detection for Gas Stations Based on Haar-AdaBoosting and CNN,” IEEE Sens. J., vol. 17, no. 19, pp. 6360–6367, 2017.
[35] H. Xie, Q. Wu, B. Chen, Y. Chen, and S. Hong, “Vehicle Detection in Open Parks Using a Convolutional Neural Network,” Proc. - 2015 6th Int. Conf. Intell. Syst. Des. Eng. Appl. ISDEA 2015, pp. 927–930, 2016.

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