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http://dx.doi.org/10.12815/kits.2018.17.5.173

Parking Lot Vehicle Counting Using a Deep Convolutional Neural Network  

Lim, Kuoy Suong (Dept. of Computer Eng., Univ. of Inha)
Kwon, Jang woo (Dept. of Computer Eng., Univ. of Inha)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.17, no.5, 2018 , pp. 173-187 More about this Journal
Abstract
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.
Keywords
Parking lot management; Object detection; Computer vision; Machine learning; Deep convolutional neural network; Surveillance camera;
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