Park Smart

The paper presents Park Smart, a solution which aim is to solve the pain of finding a free parking space in public and private areas (e.g. cities, malls, etc.), and hence to optimize parking stalls allocation as well as to increase revenues for the companies which manage them. The proposed solution exploits cutting edge technologies such as IoT, Cloud Computing and Deep Learning.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Ejaz Ahmed,et al.  A survey on mobile edge computing , 2016, 2016 10th International Conference on Intelligent Systems and Control (ISCO).

[4]  Marc Tschentscher,et al.  Video-based parking space detection , 2012 .

[5]  Carlo Meghini,et al.  Deep learning for decentralized parking lot occupancy detection , 2017, Expert Syst. Appl..

[6]  Giovanni Maria Farinella,et al.  Learning Approaches for Parking Lots Classification , 2016, ACIVS.

[7]  Giovanni Maria Farinella,et al.  An integrated system for vehicle tracking and classification , 2015, Expert Syst. Appl..

[8]  Luiz Eduardo Soares de Oliveira,et al.  PKLot - A robust dataset for parking lot classification , 2015, Expert Syst. Appl..

[9]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[10]  I. I Ntroduction,et al.  Intelligent Parking Space Detection System Based on Image Processing , 2012 .

[11]  Nicholas True Vacant Parking Space Detection in Static , 2007 .

[12]  Qi Wu,et al.  Robust Parking Space Detection Considering Inter-Space Correlation , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[13]  Sebastiano Battiato,et al.  Depth map generation by image classification , 2004, IS&T/SPIE Electronic Imaging.

[14]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[15]  Lih Lin Ng,et al.  Vision-based activities recognition by trajectory analysis for parking lot surveillance , 2012, 2012 IEEE International Conference on Circuits and Systems (ICCAS).

[16]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.