NDLI: Vehicular traffic density estimation via statistical methods with automated state learning

Content Provider	IEEE Xplore Digital Library
Author	Tan, E. Jing Chen
Copyright Year	2007
Description	Author affiliation: Nat. ICT Australia (NICTA), Sydney (Tan, E.; Jing Chen)
Abstract	This paper proposes a novel approach of combining an unsupervised clustering scheme called AutoClass with Hidden Markov Models (HMMs) to determine the traffic density state in a Region Of Interest (ROI) of a road in a traffic video. Firstly, low-level features are extracted from the ROI of each frame. Secondly, an unsupervised clustering algorithm called AutoClass is then applied to the low-level features to obtain a set of clusters for each pre-defined traffic density state. Finally, four HMM models are constructed for each traffic state respectively with each cluster corresponding to a state in the HMM and the structure of HMM is determined based on the cluster information. This approach improves over previous approaches that used Gaussian Mixture HMMs (GMHMM) by circumventing the need to make an arbitrary choice on the structure of the HMM as well as determining the number of mixtures used for each density traffic state. The results show that this approach can classify the traffic density in a ROI of a traffic video accurately with the property of being able to handle the varying illumination elegantly.
Starting Page	164
Ending Page	169
File Size	373104
Page Count	6
File Format	PDF
ISBN	9781424416950
DOI	10.1109/AVSS.2007.4425304
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-05
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Statistical analysis Roads Hidden Markov models Traffic control Cities and towns Feature extraction Cameras Australia State estimation Vehicles
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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