NDLI: A Novel Approach of Extracting Traffic Parameters by Using Video Features

Content Provider	IEEE Xplore Digital Library
Author	Yuan Zhang Kebin Jia
Copyright Year	2013
Description	Author affiliation: Coll. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China (Yuan Zhang; Kebin Jia)
Abstract	Intelligent Transportation System is a worldwide research hotspot and the extraction of traffic parameters is a crucial part of it for subsequent identification of traffic states. This paper proposes a novel approach of extracting traffic parameters such as time occupancy, volume and vehicle velocity based on video images. Visual features obtained from spatio-temporal images are more immune to environmental variations which including illuminations and background. Also binaryzation with Self-adaptive Threshold based on clustering can segment vehicle areas more accurately. With combination of parameters modification, PVI and EPI analysis serve to extract final parameters even when congestion happens. To testify the efficacy of measurement, extracted parameters are input to classifier of Support Vector Machine (SVM) to identify four levels of traffic states, which are fluent, non-congestion, congestion and terrible congestion respectively. Experimental results show that performance can sustain various environmental conditions and the accuracy is robust in heavy traffic states.
Starting Page	230
Ending Page	233
File Size	276269
Page Count	4
File Format	PDF
ISBN	9780769551203
DOI	10.1109/IIH-MSP.2013.66
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	PVI Identification of Traffic States Roads Educational institutions STI SVM Traffic Parameters Vehicles Support vector machines Accuracy Hidden Markov models Feature extraction EPI
Content Type	Text
Resource Type	Article

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