Optimized outdoor parking system for smart cities using advanced saliency detection method and hybrid features extraction model

References

• Zoika S, Panagiotis GT, Stefanos T, et al. Casual analysis of illegal parking in urban roads: the case of Greece. Case Stud Transp Policy. 2021;9(3):1084–1096.
   Web of Science ®Google Scholar
• AI-Kharusi H, Al-Bahadly I. Intelligent parking management system based on image processing. World J Eng Technol. 2014;2:55–67.
   Google Scholar
• Tian Q, Guo T, Qiao S. Design of intelligent parking management system based on license plate recognition. J Multimed. 2014;9(6):774–780.
   Google Scholar
• Yusnita R, Norbaya F, Basharuddin N. Intelligent parking space detection system based on image processing. Int J Innov Manag Technol. 2012;3(3):232–235.
   Google Scholar
• Faheem F, Mahmud SA, Khan GM, et al. A survey of intelligent car parking system. J Appl Res Technol. 2013;11(5):714–726.
   Google Scholar
• Rizwan M, Habib HA. Video analytics framework for automated parking. Tech J, Univ Eng Technol (UET) Taxila, Pakistan. 2015;20(4):87–94.
   Google Scholar
• Alharbi A, Ahlkias G, Yamin M, et al. Web based frame work for smart parking system. Int J Inf Technol. 2021;13:1495–1502.
   Google Scholar
• Postigo CG, Torres J, Menendez JM. Vacant parking area estimation through background subtraction and transience map analysis. IET Intell Transp Syst. 2015;9(9):835–841.
   Web of Science ®Google Scholar
• Almeida P, Oliveira LS, Silva E, et al. Parking space detection using textural descriptors. In: Systems, man, and cybernetics (SMC), IEEE international conference, 2013. p. 3603–3608.
   Google Scholar
• Do H, Kim J, Lee K, et al. Implementation of CNN based parking slot type classification using arounf view image. In: Proceedings of the 2020 IEEE international conference on consumer electronics (ICCE), 2020.
   Google Scholar
• Tschentscher M, Koch C, König M, et al. Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms. In: Proceedings of the international joint conference on neural networks, vol. 15, 2015.
   Google Scholar
• Ahrnbom M, Astrom K, Nilsson M. Fast classification of empty and occupied parking spaces using integral channel features. In: IEEE computer society conference on computer vision and pattern recognition workshops, 2016. p. 1609–1615.
   Google Scholar
• Delibaltov D, Wu W, Loce RP, et al. Parking lot occupancy determination from lamp-post camera images. In: IEEE conference on intelligent transportation systems, proceedings, ITSC, 2013. p. 2387–2392.
   Google Scholar
• Wu Q, Huang C, Wang S, et al. Robust parking space detection considering inter-space correlation. In: IEEE International conference on multimedia and expo, 2007. p. 659–662.
   Google Scholar
• Seo YW, Urmson C. Utilizing prior information to enhance self-supervised aerial image analysis for extracting parking lot structures. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems, pp. 339–344, 2009.
   Google Scholar
• Lu R, Lin X, Zhu H, et al. SPARK: a new VANET-based smart parking scheme for large parking lots. In: Proceedings – IEEE INFOCOM, 2009. p. 1413–1421.
   Google Scholar
• Geng Y, Cassandras CG. A new ‘smart parking’ system based on optimal resource allocation and reservations. International IEEE conference on intelligent transportation systems, Washington, DC, USA, vol. 14, 2011.
   Google Scholar
• Tang C, Wei X, Zhu C, et al. Towards smart parking based on fog computing. IEEE Access. 2018;6:70172–70185.
   Web of Science ®Google Scholar
• Badii C, Nesi P, Paoli I. Predicting available parking slots on critical and regular services by exploiting a range of open data. IEEE Access. 2018;6:44059–44071.
   Web of Science ®Google Scholar
• Wolff J, Heuer T, Gao H, et al. Parking monitor system based on magnetic field sensors. In: IEEE intelligent transportation systems conference, Toronto, Canada, 2006. p. 1275–1279.
   Google Scholar
• Chunhe Y, Jilin L. A type of sensor to detect occupancy of vehicle berth in carpark. In: Proceedings of international conference on signal processing, vol. 3, 2004. p. 2708–2713.
   Google Scholar
• Kotb AO, Shen YC, Huang Y. Smart parking guidance, monitoring and reservations: a review. IEEE Intell Transp Syst Mag. 2017;9(2):6–16.
   Web of Science ®Google Scholar
• Safi QGK, Luo S, Pan L, et al. SVPS: cloud-based smart vehicle parking system over ubiquitous VANETs. Comput Netw. 2018;138:18–30.
   Web of Science ®Google Scholar
• Huang CC, Wang SJ. A hierarchical Bayesian generation framework for vacant parking space detection. IEEE Trans Circuits Syst Video Technol. 2011;20(12):1770–1785.
   Web of Science ®Google Scholar
• Funck S, Mohler N, Oertel W. Determining car-park occupancy from single images. In: Proceedings of IEEE intelligent vehicles symposium, 2004. p. 325–328.
   Google Scholar
• Lee CH, Wen MG, Han CC, et al. An automatic monitoring approach for unsupervised parking lots in outdoors. In: Proceedings of international carnahan conference on security technology, 2005. p. 271–274.
   Google Scholar
• Horprasert T, Harwood D, Davis LS. A statistical approach for real-time robust background subtraction and shadow detection. In: IEEE ICCV, 1999.
   Google Scholar
• Schneiderman H, Kanade T. Object detection using the statistics of parts. Int J Comput Vision. 2004;56:151–177.
   Web of Science ®Google Scholar
• Viola P, Jones M. Robust real-time face detection. Int J Comput Vision. 2004;57(2):137–154.
   Web of Science ®Google Scholar
• Huang CC, Tai YS, Wang SJ. Vacant parking space detection based on plane-based Bayesian hierarchical framework. IEEE Trans Circuits Syst Video Technol. 2013;23(9):1598–1610.
   Web of Science ®Google Scholar
• Manase DK, Zainuddin Z, Syarif S, et al. Car detection in roadside parking for smart parking system based on image processing. In: 2020 international seminar on intelligent technology and its applications (ISITIA), IEEE, 2020. p. 194–198.
   Google Scholar
• Caicedo F, Blazquez C, Miranda P. Prediction of parking space availability in real time. Expert Syst Appl. 2012;39(8):7281–7290.
   Web of Science ®Google Scholar
• Saharan S, Kumar N, Bawa S. An efficient smart parking pricing system for smart city environment: a machine-learning based approach. Future Gener Comput Syst. 2020;106(1):622–640.
   Google Scholar
• Jermsurawong J, Ahsan U, Haidar A, et al. One-day long statistical analysis of parking demand by using single-camera vacancy detection. J Transp Syst Eng Inf Technol. 2014;14(2):33–44.
   Google Scholar
• Kaur S, Bansal RK, Mittal M, et al. Mixed pixel decomposition based on extended fuzzy clustering for single spectral value remote sensing images. J Indian Soc Remote Sens. 2019;47(3):427–437. doi:10.1016/j.compeleceng.2018.08.018.
   Web of Science ®Google Scholar
• Mittal M, Kumar M, Verma A, et al. FEMT: a computational approach for fog elimination using multiple thresholds. Multimed Tools Appl. 2021;80(1):227–241.
   Web of Science ®Google Scholar
• Singh R, Gahlot A, Mittal M. Iot based intelligent robot for various disasters monitoring and prevention with visual data manipulating. Int J Tomogr Simul. 2019;32(1):90–99.
   Google Scholar
• Sanjeewa EDG, Herath KKL, Madhusanka BGDA, et al. Visual attention model for mobile robot navigation in domestic environment. GSJ. 2020;8(7):1960–1965.
   Google Scholar
• Ghansiyal A, Mittal M, Kar AK. Information management challenges in autonomous vehicles: a systematic literature review. J Cases Inf Technol. 2021;23(3):58–77.
   Web of Science ®Google Scholar
• Kumar I, Manuja P, Soni Y, et al. An integrated approach toward smart parking implementation for smart cities in India. In: Kolhe M, Tiwari S, Trivedi M, et al., editors. Advances in data and information sciences. Vol. 94. Singapore: Springer; 2020. p. 343–349. (Lecture notes in networks and systems).
   Google Scholar
• Nath A, Konwar HN, Kumar K, et al. Technology enabled smart efficiency parking system (TESEPS). In: Sarma, H, Bhuyan B, Borah S, et al., editors. Trends in communication, cloud, and big data. Vol. 99. Singapore: Springer; 2020. p. 141–150. (Lecture notes in networks and systems).
   Google Scholar
• da Cruz MAA, Rodrigues JJ, Gomes GF, et al. An IoT-based solution for smart parking. In: Singh P, Pawłowski W, Tanwar S, et al., editors. Proceedings of first international conference on computing, communications, and cyber-security (IC4S 2019). Vol. 121. Singapore: Springer; 2020. p. 213–224. (Lecture notes in networks and systems).
   Google Scholar
• Sarangi M, Mohapatra S, Tirunagiri SV, et al. IoT aware automatic smart parking system for smart city. In: Mallick P, Balas V, Bhoi A, et al., editors. Cognitive informatics and soft computing. Vol. 1040. Singapore: Springer; 2020. p. 469–481. (Advances in intelligent systems and computing).
   Google Scholar
• Irmak E, Ertas AH. A review of robust image enhancement algorithms and their applications. In: IEEE International conference on smart energy grid engineering, 2016. p. 371–375.
   Google Scholar
• Jaya VL, Gopikakumari R. IEM: a new image enhancement metric for contrast and sharpness measurements. Int J Comput Appl. 2013;79(9):1–9.
   Google Scholar
• Saenpaen J, Arwatchananukul S, Aunsri N. A comparison of image enhancement methods for lumbar spine X-ray image. In: International conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), 2018. p. 798–801.
   Google Scholar
• Jintasuttisak T, Intajag S. Color retinal image enhancement by Rayleigh contrast-limited adaptive histogram equalization. Int Conf Control Autom Syst. 2014;10:692–697.
   Google Scholar
• Yadav G, Maheshwari S, Agarwal A. Multi-domain image enhancement of foggy images using contrast limited adaptive histogram equalization method. In: Proceedings of international conference on recent cognizance in wireless communication & image processing, Springer, 2014. p. 31–38.
   Google Scholar
• Elaziz MA, Bhattacharyya S, Lu S. Swarm selection method for multilevel thresholding image segmentation. Expert Syst Appl. 2019;138:1–24.
   Web of Science ®Google Scholar
• Yu C-y, Zhang W-s, Wang C-l. A saliency detection method based on global contrast. Int J Signal Process Image Process Pattern Recognit. 2015;8(7):111–122.
   Google Scholar
• Bali A, Singh SN. A review on the strategies and techniques of image segmentation, In: IEEE international conference on advanced computing & communication technologies, 2015. p. 113–120.
   Google Scholar
• Kang WX, Yang QQ, Liang RR. The comparative research on image segmentation algorithms. In: IEEE conference on education technology and computer science, 2009. p. 703–707.
   Google Scholar
• Yogamangalam R, Karthikeyan B. Segmentation techniques comparison in image processing. Int J Eng Technol. 2013;5:307–313.
   Google Scholar
• Min H, Jia W, Wang XF, et al. An intensity-texture model based level set method for image segmentation. Pattern Recognit. 2015;48:1547–1562.
   Web of Science ®Google Scholar
• Zhang J, Sclaroff S. Saliency detection: a Boolean map approach. In: Proceedings of IEEE international conference on computer vision, 2013. p. 153–160.
   Google Scholar
• Karch P, Zolotova I. An experimental comparison of modern methods of segmentation. In: IEEE international symposium on applied machine intelligence and informatics (SAMI), 2010. p. 247–252.
   Google Scholar
• Qi W, Han J, Zhang Y, et al. Graph-Boolean map for salient object detection. Signal Process Image Commun. 2016;49:9–16.
   Web of Science ®Google Scholar
• Cong R, Lei J, Fu H, et al. Review of visual saliency detection with comprehensive information. IEEE Trans Circuits Syst Video Technol. 2018;29(10):2941–2959.
   Web of Science ®Google Scholar
• Zhang L, Chen J, Qiu B. Region of interest extraction in remote sensing images by saliency analysis with the normal directional lifting wavelet transform. Neurocomputing. 2016;179:186–201.
   Web of Science ®Google Scholar
• Zhang F, Wu T, Zheng G. Video salient region detection model based on wavelet transform and feature comparison. EURASIP J Image Video Process. 2019;58.
   Google Scholar
• Zhang J, Sclaroff S. Saliency detection: a Boolean map approach. In: Proceedings of IEEE international conference on computer vision, 2013. p. 153–160.
   Google Scholar
• Koffka K. Principles of Gestalt psychology. London: Lund Humphries; 1935.
   Google Scholar
• Kootstra G, Kragic D. Fast and bottom-up object detection, segmentation, and evaluation using Gestalt principles. In: Proceedings of IEEE international conference on robotics and automation, 2011. p. 3423–3428.
   Google Scholar
• Lin CH, Chen RT, Chan YK. A smart content-based image retrieval system based on color and texture feature. Image Vis Comput. 2009;27(6):658–665.
   Web of Science ®Google Scholar
• Chen WT, Liu WC, Chen MS. Adaptive color feature extraction based on image color distributions. IEEE Trans Image Process. 2010;19(8):2005–2016.
   PubMed Web of Science ®Google Scholar
• Aptoula E, Lefevre S. Morphological description of color images for content-based image retrieval. IEEE Trans Image Process. 2009;18(11):2505–2517.
   PubMed Web of Science ®Google Scholar
• Alqaisi A, Altarawneh M, Alqadi ZA, et al. Analysis of color image features extraction using texture methods. Telecommun Comput Electron Control. 2019;17(3):1220–1225.
   Google Scholar
• Humeau-Heurtier A. Texture feature extraction methods: a survey. In: IEEE access. Vol. 7. IEEE; 2019. p. 8975–9000.
   Web of Science ®Google Scholar
• Navarro CF, Perez CA. Color–texture pattern classification using global–local feature extraction, an SVM classifier, with bagging ensemble post-processing. Appl Sci. 2019;9(15):3130.
   Google Scholar
• Singha M, Hemachandran K. Content based image retrieval using color and texture. Signal Image Process: An Int J. 2012;3(1):39–57.
   Google Scholar
• Wang X, Bai X, Liu W, et al. Feature context for image classification and object detection. In: Proceedings of IEEE international conference of computer vision and pattern recognition, 2011. p. 961–968.
   Google Scholar
• Wu CM, Chen YC. Statistical feature matrix for texture analysis. CVGIP, Graph Models Image Process. 1992;54(5):407–419.
   Google Scholar
• Haralick RM. Statistical and structural approaches to texture. Proc IEEE. 1979;67(5):786–804.
   Web of Science ®Google Scholar
• Ahmad WSHMW, Fauzi MFA. Comparison of different feature extraction techniques in content-based image retrieval for CT brain images. IEEE, 2008.
   Google Scholar
• Huang S, Cai N, Pacheco PP, et al. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteom. 2018;15:41–51.
   PubMed Web of Science ®Google Scholar
• Yu W, Liu T, Valdez R, et al. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes”. BMC Med Inform Decis Mak. 2010;10(16).
   PubMedGoogle Scholar
• Zhu W, Zeng N, Wang N. Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS® implementations. Health Care and Life Sciences, NESUG 2010.
   Google Scholar
• MATLAB R2018a on Windows 7 Professional, 64-bit Operating System, Processor Intel (R) Core (TM) i3-5005U CPU @ 2.00GHz, RAM 12.0 GB.
   Google Scholar