Vacant Parking Lot Information System Using Transfer Learning and IoT

Edwin K Jose, S. Veni


Parking information systems have become very important, especially in metropolitan areas as they help to save time, effort and fuel when searching for parking. This paper offers a novel low-cost deep learning approach to easily implement vacancy detection at outdoor parking spaces with CCTV surveillance. The proposed method also addresses issues due to perspective distortion in CCTV images. The architecture consists of three classifiers for checking the availability of parking spaces. They were developed on the TensorFlow platform by re-training MobileNet (a pre-trained Convolutional Neural Network (CNN)) model using the transfer learning technique. A performance analysis showed 88% accuracy for vacancy detection. An end-to-end application model with Internet of Things (IoT) and an Android application is also presented. Users can interact with the cloud using their Android application to get real-time updates on parking space availability and the parking location. In the future, an autonomous car could use this system as a V2I (Vehicle to Infrastructure) application in deciding the nearest parking space.


Android application; CCTV surveillance; CNN; IoT; MobileNet; outdoor parking; TensorFlow; transfer learning; V2I

Full Text:



Roychowdhury, A. & Nasim, U., Parking Policy for Clean Air & Liveable Cities: A Guidance Framework, Centre for Science and Environment, New Delhi, India, 2016.

Dowling, C., Fiez, T., Ratliff, L. & Zhang, B., How Much Urban Traffic is Searching for Parking, arXiv:1702.06156 [cs.CY], Submitted to applications track to KDD 2017.

Faheem S.A., Mahmud G.M., Khan M. & Zafar, R.H., A Survey of Intelligent Car Parking System, Journal of Applied Research and Technology, 11(5), pp. 714-726, 2013.

Murugesan, G., Vasudevan, S.K., Ramachandran, S., Vasudevan, S. & Arumugam, B., Vehicle Identification using Fuzzy Adaline Neural Network, Journal of Computer Science, 9(6), pp. 757-762, 2013.

Wang, J., Zheng, H., Huang, Y. & Ding, X., Vehicle Type Recognition in Surveillance Images from Labeled Web-Nature Data Using Deep Transfer Learning, IEEE Transactions on Intelligent Transportation Systems, PP (99) (IEEE Early Access), pp. 1-10, 2017.

Sreedevi, A.P. & Nair, B.S.S., Image Processing Based Real Time Vehicle Theft Detection and Prevention System, in Proceedings of International Conference on Process Automation, Control and Computing (PACC), 2011.

Huang, C.C. & Vu, H.T., Vacant Parking Space Detection Based on a Multilayer Inference Framework, IEEE Trans. CSVT, 27(9), pp. 2041-2054, 2017.

Amato, G., Carrara, F., Falchi, F., Gennaro, C. & Vairo, C., Car Parking Occupancy Detection Using Smart Camera Networks and Deep Learning, in IEEE Symposium on Computers and Communication (ISCC), pp. 1212-1217, 2016.

Xia, X., Xu, C. & Nan, B., Inception-v3 for Flower Classification Image, in 2nd International Conference on Vision and Computing (ICIVC), pp. 783-787, 2017.

Almeida, P.R. de, Oliveira, L.S., Britto, A.S., Silva, E. J. & Koerich, A.L., Pklot – A Robust Dataset for Parking Lot Classification, Expert Systems with Applications, 42(11), pp. 4937-4949, 2015.

Al-Kharusi, H. & Al-Bahadly, I., Intelligent Parking Management System Based on Image Processing, World Journal of Engineering and Technology, 2(2), pp. 55-67, 2014.

Postigo, C.G. del, Torres, J. & Menéndez, J.M., Vacant Parking Area Estimation Through Background Subtraction and Transience Map Analysis, IET Intelligent Transport Systems, 9(9), pp. 835 -841, 2015.

Ling, X., Sheng, J., Baiocchi, O., Liu, X. & Tolentino, M.E., Identifying Parking Spaces & Detecting Occupancy Using Vision-Based IoT Devices, in Global Internet of Things Summit (GIoTS), pp. 1-6, 2017.

Fraifer, M. & Fernström, M., Smart Car Parking System Prototype Utilizing CCTV Nodes: A Proof of Concept Prototype of a Novel Approach Towards IoT-Concept Based Smart Parking, in IEEE 3rd World Forum on Internet of Things (WF-IoT), pp. 649-654, 2016.

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. & Rabinovich, A., Going Deeper with Convolutions, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, 2015.

Jia, J., Dong, D.W., Socher, R., Li,Kai Li, L.J. & Fei-Fei, L., ImageNet: A Large-Scale Hierarchical Image Database, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, 2009.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z., Rethinking the Inception Architecture for Computer Vision, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818-2826, 2016.

Gogul, I. & Sathiesh Kumar, V., Flower Species Recognition System Using Convolution Neural Networks and Transfer Learning, in Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), pp. 1-6, 2017.

Krizhevsky, A., Sutskever, I. & Hinton, G.E., ImageNet Classification with Deep Convolutional Neural Networks, in Proc. Neural Inf. Process. Syst. (NIPS), pp. 1097-1105, 2012.

Dar, S.U.H. & Çukur, T., A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks, arXiv:1710.02615v2 [cs.CV], 2017.

Christodoulidis, S., Anthimopoulos, M., Ebner, L., Christe, A. & Mougiakakou, S., Multisource Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis, IEEE Journal of Biomedical and Health Informatics, 21(1), pp. 76-84, 2017.

Huang, Z., Pan, Z. & Lei, B., Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data, Remote Sensing MDPI, 9(9), pp. 1-21, 2017.

Howard Andre, G., Menglong, X., Bo, C., Dmitry, K., Zeijun, W., Tobias, W., Marco, A. & Hartwig, A., MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv:1704.04861v1[cs.CV],2017. (The model can be downloaded from

-for.html. (Last accessed:01-Dec-2017)

Pan, S.J. & Yang, Q., A Survey on Transfer Learning, 22(10), pp. 1345-1359, 2010.

Yosinski, J., Clune, J., Bengio, Y. & Lipson, H., How Transferable Are Features in Deep Neural Networks, in Proc. Neural Inf. Process. Syst. (NIPS), pp. 3320-3328, 2014.

Razavian, A.S., Azizpour, H., Sullivan, J. & Carlsson, S., CNN Features Off-The-Shelf: An Astounding Baseline for Recognition, Proc. 2014 IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, pp. 512-519, 2014.

Abadi, M. & Agarwal, A., TensorFlow: Large-scale Machine Learning on Heterogeneous Distributed Systems. arXiv:1603.04467v2[cs.DC], 2016.

Alpaydin, E., Introduction to Machine Learning, 3rd ed., The MIT Press, Massachusetts, 2014.

Goodfellow, I., Bengio, Y. & Courville, A., Deep Learning, The MIT Press, Massachusetts, 2016.



  • There are currently no refbacks.

Contact Information:

ITB Journal Publisher, LPPM – ITB, 

Center for Research and Community Services (CRCS) Building Floor 7th, 
Jl. Ganesha No. 10 Bandung 40132, Indonesia,

Tel. +62-22-86010080,

Fax.: +62-22-86010051;