NDLI: Vehicles detection in complex urban traffic scenes using Gaussian mixture model with confidence measurement

Content Provider	IET Digital Library
Author	Zhang, Yunsheng Zhao, Chihang He, Jie Chen, Aiwei
Abstract	Aiming to efficiently resolve the problem that the subtraction background model is easily contaminated by slow-moving or temporarily stopped vehicles, the Gaussian mixture model with confidence measurement (GMMCM) is proposed for vehicle detection in complex urban traffic scenes. According to the current traffic state, each pixel of background model is set a CM. Whether to update the background model and the corresponding adaptive learning rate depends on if the current pixel point is in confidence period. Using the real-world urban traffic videos, the first experiments are conducted by GMMCM, compared with three commonly used models including GMM, self-adaptive GMM (SAGMM) and local parameter learning algorithm for the GMM (LPLGMM). The first experimental results show that GMMCM excels GMM, SAGMM and LPLGMM in keeping the background model being unpolluted from slow-moving or temporarily stopped vehicles. The second experiments are conducted by GMMCM, compared with visual background extractor, sigma-delta with CM, SAGMM, LPLGMM and GMM. The average recalls of six methods are 0.899, 0.753, 0.679, 0.420, 0.447 and 0.205, and the average F-measures of six methods are 0.636, 0.612, 0.592, 0.373, 0.330 and 0.179, respectively. All experimental results show the effectiveness of the proposed GMMCM in vehicles detection of complex urban traffic scenes.
Starting Page	445
Ending Page	452
Page Count	8
ISSN	1751956X
Volume Number	10
e-ISSN	17519578
Issue Number	Issue 6, Aug (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/10/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2015.0141
Journal	IET Intelligent Transport Systems
Publisher Date	2016-08-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Complex Urban Traffic Scenes Computer Vision And Image Processing Technique Confidence Measurement Gaussian Mixture Model with Confidence Measurement Gaussian Processes GMMCM Image Processing Mixture Model Optical, Image And Video Signal Processing Road Traffic Road Vehicle Statistics Subtraction Background Model Traffic Engineering Computing Vehicle Detection Visual Background Extractor
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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