NDLI: Automated recognition of drunk driving on highways from video sequences

Content Provider	IEEE Xplore Digital Library
Author	Carswell, B. Chandran, V.
Copyright Year	1994
Description	Author affiliation: Signal Process. Res. Centre, Queensland Univ., Brisbane, Qld., Australia (Carswell, B.; Chandran, V.)
Abstract	A new method for the detection of abnormal vehicle trajectories is proposed. It couples optical flow extraction of vehicle velocities with a neural network classifier. Abnormal trajectories are indicative of drunk or sleepy drivers. A single feature of the vehicle, e.g., a tail light, is isolated and the optical flow computed only around this feature rather than at each pixel in the image. The velocity fields are accurately extracted using a modification of the basic optical flow method (Horn and Schunck, 1981, and Barron et al., 1994). Trajectories are extracted in the form of direction of motion in each frame. A back-propagation neural network is used to classify the vehicle trajectories as either normal or abnormal. The neural network is shown to perform accurate classification on simulated trajectories.
Starting Page	306
Ending Page	310
File Size	388523
Page Count	5
File Format	PDF
ISBN	0818669527
DOI	10.1109/ICIP.1994.413581
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-11-13
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automated highways Video sequences Image motion analysis Optical filters Optical signal processing Optical computing Neural networks Vehicles Data mining Brightness
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
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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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