NDLI: Image Sequences Based Traffic Incident Detection for Signaled Intersections Using HMM

Content Provider	IEEE Xplore Digital Library
Author	Yuexian Zou Guangyi Shi Hang Shi Yiyan Wang
Copyright Year	2009
Abstract	For an intelligent transportation system (ITS), traffic incident detection is one of the most important issues, especially for urban area which is full of signaled intersections. In this paper, we propose a novel traffic incident detection method based on the image signal processing and hidden Markov model (HMM) classifier. First, a traffic surveillance system was set up at a typical intersection of china, traffic videos were recorded and image sequences were extracted for image database forming. Second, compressed features were generated through several image processing steps, image difference with FFT was used to improve the recognition rate. Finally, HMM was used for classification of traffic signal logics (East-West, West-East, South-North, North-South) and accident of crash, the total correct rate is 74% and incident recognition rate is 84%. We believe, with more types of incident adding to the database, our detection algorithm could serve well for the traffic surveillance system.
Starting Page	257
Ending Page	261
File Size	376494
Page Count	5
File Format	PDF
ISBN	9780769537450
DOI	10.1109/HIS.2009.58
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intersection Urban areas Incident Detection ITS HMM Image sequences Intelligent transportation systems Video signal processing Image coding Image databases Surveillance Hidden Markov models Traffic control Signal detection
Content Type	Text
Resource Type	Article

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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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