A machine learning technique for detecting outdoor parking

  • Authors

    • Neeru Mago
    • Dr Satish Kumar
    2018-05-29
    https://doi.org/10.14419/ijet.v7i2.30.13460
  • Parking Management System, Video Analytics, Image Processing, Feature Extraction, Machine Learning, Hybrid Model.

  • Abstract

    In recent years, it has been observed that it becomes time-consuming and cumbersome job to find a vacant parking lot, especially in urban areas. Thus, it makes difficult for potential visitors or customers to search a vacant space for parking their vehicles and keeps on revolving round the parking area which not only increases frustration level but also wastes time and energy. In order to get an optimal parking lot immediately, there is a requirement of an efficient car-park routing systems. Current systems detecting vacant parking lots are either based on very expensive sensor based technology; or based on video based technologies which do not consider various weather conditions like sunny, cloudy and rainy weather. In the proposed work, a hybrid model is designed for detecting outdoor parking which detects the empty spaces available in the parking lots and the spaces/ slots getting vacant in the real-time scenario. This model is based on training, validating and testing the images (dataset) collected from various heights and angles of different parking areas stored in the repository. In this research, more advanced feature extractors and machine learning algorithms are evaluated in order to find the vacant parking lots in the outdoor park-ing areas.

     

     

  • References

    1. [1] Paulo et.al, PKLot – A robust dataset for parking lot classification, Expert Systems with Applications 42 (2015), pg. 4937–4949.

      [2] Al-Kharusi et.al, (2014) Intelligent Parking Management System Based on Image Processing. World Journal of Engineering and Technology, 2, 55-67.

      [3] Qing Tian et.al.,Design of Intelligent Parking Management System Based on License Plate Recognition. Journal of Multimedia, Vol. 9, No.6, June 2014, pg 774-780.

      [4] Yusnita et.al., Intelligent Parking Space Detection System Based on Image Processing. International Journal of Innovation, Management and Technology, Vol. 3, No. 3, June 2012, pg 232-235.

      [5] Rizwan et.al., Video Analytics Framework for Automated Parking. Technical Journal, University of Engineering and Technology (UET) Taxila, Pakistan Vol. 20 No.IV-2015, pg 87-94.

      [6] Tang et.al., An Intelligent Car Park Management System based on Wireless Sensor Networks. 2006 1st International Symposium on Pervasive Computing and Applications, pg 65-70.

      [7] Catherine Wah, Parking Space Vacancy Monitoring, University of California, San Diego 9500 Gilman Drive, La Jolla, CA 92093 cwah@cs.ucsd.edu

      [8] Carlos Gálvez et.al., Vacant parking area estimation through background subtraction and transience map analysis.

      [9] Diana Delibaltov et.al., Parking Lot Occupancy Determination from Lamp Post Camera Images.

      [10] Gupta, Analysis of Real-Time Big Data: Its Applications and Challenges. Journal of Data Mining and Management Volume 1 Issue 2, Page 1-10 © MAT Journals 2016.

      [11] ZhengXu et.al., The big data analytics and applications of the surveillance system using video structured description technology, Cluster Compute (2016) 19:1283–1292 DOI 10.1007/s10586-016-0581-x.

      [12] Hampapur et.al., Video Analytics in Urban Environments.2009 Advanced Video and Signal Based Surveillance, pg 128-133.

      [13] Jing Li et. Al., Real-time Video Copy Detection Based on Hadoop, Sixth International Conference on Information Science and Technology Dalian, China, May6-8, 2016, pg 492-497.

      [14] Banaei et.al., H.K. (2014) Hadoop and Its Role in Modern Image Processing. Open Journal of Marine Science, 4, 239-245. http://dx.doi.org/10.4236/ojms.2014.44022

      [15] Natarajan and et.al , Scalable Traffic Video Analytics using Hadoop MapReduce, All Data 2015 : The First International Conference on Big Data, Small Data, Linked Data and Open Data, ISBN: 978-1-61208-445-9, pg 11-15.

      [16] Singh et.al., Intelligent Parking Management System Using RFID, Proceedings of Fifth International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 436, DOI 10.1007/978-981-10-0448-3_41, pg no. 497-505.

      [17] Parsola et.al., Efficient Storage and Processing of Video Data for Moving Object Detection Using Hadoop/MapReduce, Proceedings of the International Conference on Signal, Networks, Computing, and Systems, Lecture Notes in Electrical Engineering 395, DOI 10.1007/978-81-322-3592-7 14, pg 137-147.

      [18] Tan et.al., An approach for fast and parallel video processing on Apache Hadoop clusters, https://www.researchgate.net/publication/264862451

      [19] Hwan et.al, Real Time Video Data Mining for Surveillance Video Streams.

      [20] Anuar et.al, OpenCV based Real-Time Video Processing using Android Smartphone, International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 1, Issue 3, ISSN 2249-6343, pg 58-63.

      [21] Parida et.al, Detection of objects in a video of traffic, International Research Journal of Engineering and Technology (IRJET) Volume: 03 Issue: 05 | May-2016 www.irjet.net p-ISSN: 2395-0072, e-ISSN: 2395 -0056, pg 676-678.

      [22] Naika et.al, Design and Analysis of Real Time Video Processing Based on DWT Architecture for Mobile Robots, International Conference on Information Security & Privacy (ICISP2015), 11-12 December 2015, Nagpur, INDIA, Available online at www.sciencedirect.com, Procedia Computer Science 78 ( 2016 ) 544 – 549.

      [23] M. Kreković et.al, A method for real-time detection of human fall from video, MIPRO 2012, May 21-25,2012, Opatija, Croatia, pg 1709-1712.

      [24] Jamkhandikar et.al, Object Detection and Tracking in Real Time Video Based on Color, International Journal of Engineering Research and Development e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com Volume 10, Issue 6 (June 2014), PP.33-37.

      [25] Prasad et.al, A Review on Object Detection in Video Processing, International Journal of u- and e- Service, Science and Technology Vol. 5, No. 4, December, 2012, pg 15-20.

  • Downloads

  • How to Cite

    Mago, N., & Satish Kumar, D. (2018). A machine learning technique for detecting outdoor parking. International Journal of Engineering & Technology, 7(2.30), 39-43. https://doi.org/10.14419/ijet.v7i2.30.13460

    Received date: 2018-05-29

    Accepted date: 2018-05-29

    Published date: 2018-05-29