Parking Lot Vehicle Counting Using a Deep Convolutional Neural Network

• Published in: The Journal of The Korea Institute of Intelligent Transport Systems Vol. 17; no. 5; pp. 173 - 187
• Main Authors: Lim, Kuoy Suong, Kwon, Jang woo
• Format: Journal Article
• Language: English
• Published: 한국ITS학회 30.10.2018
• Subjects: 전자/정보통신공학
• ISSN: 1738-0774; 2384-1729
• DOI: 10.12815/kits.2018.17.5.173

This paper proposes a computer vision and deep learning-based technique for surveillance camera system for vehicle counting as one part of parking lot management system. We applied the You Only Look Once version 2 (YOLOv2) detector and come up with a deep convolutional neural network (CNN) based on YOLOv2 with a different architecture and two models. The effectiveness of the proposed architecture is illustrated using a publicly available Udacity’s self-driving-car datasets. After training and testing, our proposed architecture with new models is able to obtain 64.30% mean average precision which is a better performance compare to the original architecture (YOLOv2) that achieved only 47.89% mean average precision on the detection of car, truck, and pedestrian. KCI Citation Count: 0