@inproceedings{cdfc50e8855b4102b0ed3722a130497d,
title = "Vehicle Detection and Classification based on Deep Neural Network for Intelligent Transportation Applications",
abstract = "This paper proposes an optimized vehicle detection and classification method based on deep learning technology for intelligent transportation applications. We optimize the Convolutional Neural Network (CNN) architecture by fine-tuning the existing CNN architecture for the intelligent transportation applications. The proposed design achieves the accuracy of miss rate around 10% when FPPI is 0.1. Realized on nVidia Titan-X GPU, the proposed design can reach the performance about 720\times 480 video under different weather condition (day, night, raining) at 25fps. The proposed model can achieve 90% accuracy on three target vehicle classes including small vehicles (Sedan, SUV, Van), big vehicles (Bus) and Trucks.",
author = "Tsai, {Chia Chi} and Tseng, {Ching Kan} and Tang, {Ho Chia} and Jiun-In Guo",
year = "2019",
month = mar,
day = "4",
doi = "10.23919/APSIPA.2018.8659542",
language = "English",
series = "2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1605--1608",
booktitle = "2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings",
address = "United States",
note = "10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 ; Conference date: 12-11-2018 Through 15-11-2018",
}