ORCID Profile
0000-0003-3288-5547
Current Organisations
University of Technology Sydney
,
University of New South Wales
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Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: IEEE
Date: 2013
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 09-2016
Publisher: ACM
Date: 19-03-2013
Publisher: British Machine Vision Association
Date: 2010
DOI: 10.5244/C.24.33
Publisher: Springer International Publishing
Date: 2018
Publisher: World Scientific Pub Co Pte Lt
Date: 11-2009
DOI: 10.1142/S0218001409007673
Abstract: Traffic flow information can be employed in an intelligent transportation system to detect and manage traffic congestion. One of the key elements in determining the traffic flow information is traffic density estimation. The goal of traffic density estimation is to determine the density of vehicles on a given road from loop detectors, traffic radars, or surveillance cameras. However, due to the inflexibility of deploying loop detectors and traffic radars, there is a growing trend of using video-content-understanding technique to determine the traffic flow from a surveillance camera. But difficulties arise when attempting to do this in real-time under changing illumination and weather conditions as well as heavy traffic congestions. In this paper, we attempt to address the problem of real-time traffic density estimation by using a stochastic model called Hidden Markov Models (HMM) to probabilistically determine the traffic density state. Choosing a good set of model parameters for HMMs has a significant impact on the accuracy of traffic density estimation. Thus, we propose a novel feature extraction scheme to represent traffic density, and a novel approach to initialize and construct the HMMs by using an unsupervised clustering technique called AutoClass. We show through extensive experiments that our proposed real-time algorithm achieves an average traffic density estimation accuracy of 96.6% over various different illumination and weather conditions.
Publisher: IEEE
Date: 09-2010
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 08-2010
DOI: 10.1109/AVSS.2010.40
Publisher: IEEE
Date: 2008
Publisher: ACM
Date: 17-10-2015
Publisher: Inderscience Publishers
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 2011
Publisher: IEEE
Date: 09-2009
DOI: 10.1109/AVSS.2009.43
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 29-06-2013
Publisher: IEEE
Date: 11-2019
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 09-2010
Publisher: Springer Science and Business Media LLC
Date: 21-05-2020
Publisher: IEEE
Date: 2011
Publisher: ACM
Date: 02-05-2019
Publisher: Association for Computing Machinery (ACM)
Date: 14-01-2015
DOI: 10.1145/2687924
Abstract: This article presents a framework of adaptive, measurable decision making for Multiple Attribute Decision Making (MADM) by varying decision factors in their types, numbers, and values. Under this framework, decision making is measured using physiological sensors such as Galvanic Skin Response (GSR) and eye-tracking while users are subjected to varying decision quality and difficulty levels. Following this quantifiable decision making, users are allowed to refine several decision factors in order to make decisions of high quality and with low difficulty levels. A case study of driving route selection is used to set up an experiment to test our hypotheses. In this study, GSR features exhibit the best performance in indexing decision quality. These results can be used to guide the design of intelligent user interfaces for decision-related applications in HCI that can adapt to user behavior and decision-making performance.
Publisher: Springer Science and Business Media LLC
Date: 08-06-2020
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 10-2008
Publisher: Elsevier BV
Date: 08-2013
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2019
No related grants have been discovered for Zhidong Li.