Adaptive Vehicle Detection and Classification Scheme for Urban Traffic Scenes Using Convolutional Neural Network

Dao Wei Yang, Hsin Tzu Wang, Yu Jung Chen, Po Chyi Su

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A large number of digital cameras have been installed at intersections in urban areas to help monitor traffic conditions. Making better use of scenes captured by these traffic surveillance cameras facilitates the construction of advanced Intelligent Transportation Systems (ITS). This research aims at developing an adaptive vehicle detection and classification scheme for urban traffic scenes, which collects roadside surveillance videos from publicly available sources. The proposed scheme consists of two main phases; the first phase is to collect some traffic surveillance images for training a general model using Faster R-CNN. The second phase utilizes background subtraction to extract vehicle proposals. A sufficient number of vehicles are collected by comparing proposals with the detection results by the general model. Collected vehicles are superimposed on the constructed background in an appropriate order to achieve semiautomatic generation of annotated training data. The training data are used to acquire a second-phase adaptive model. The experimental results show that the proposed scheme performs well and can handle vehicle occlusion problems.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728132792
DOIs
StatePublished - May 2019
Event6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019 - Yilan, Taiwan
Duration: 20 May 201922 May 2019

Publication series

Name2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019

Conference

Conference6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
Country/TerritoryTaiwan
CityYilan
Period20/05/1922/05/19

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