NDLI: Detection and identification in the intelligent traffic video monitoring system for pedestrians and vehicles

Content Provider	IEEE Xplore Digital Library
Author	Xuehua Song Liguo Wang Hong Wang Yuhua Zhang
Copyright Year	2011
Description	Author affiliation: Department of Telecommunication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China (Xuehua Song; Liguo Wang) \|\| Hents Technologies Inc, Zhenjiang, Jiangsu, China (Hong Wang; Yuhua Zhang)
Abstract	On the most of highway, tunnels and bridges, pedestrians are not allowed to access in the current public traffic management. The traditional transportation surveillance system can only monitor the scene but could not alarm automatically for the abnormity, so how to detect the pedestrians and alarm automatically when the people have access to the highway is a great challenge for the intelligent transportation video surveillance system. The paper proposes an algorithm which can solve the problems effectively by the improved Gaussian mixture model and Support vector machine. First of all, the paper introduce an improved Gaussian mixture model which can effectively detect the moving objects and resolve the problems of Gaussian mixture model sensitive to light changes. Then the paper designs some classifiers to recognize the pedestrians and vehicles by the idea of the improved SVM. The experimental results show that the method has a high recognition rate and can also satisfy the real-time intelligent transportation surveillance.
Starting Page	181
Ending Page	185
File Size	183606
Page Count	5
File Format	PDF
ISBN	9781457701856
e-ISBN	9788988678374
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-21
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	AICIT
Subject Keyword	Support vector machines Training Real time systems moving objects detection Object detection Gaussian distribution SVM Intelligent Traffic Video Monitoring System Pixel Gaussian mixture model Vehicles
Content Type	Text
Resource Type	Article

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