NDLI: Monocular target detection on transport infrastructures with dynamic and variable environments

Content Provider	IEEE Xplore Digital Library
Author	Alvarez, S. Llorca, D.F. Sotelo, M.A. Lorente, A.G.
Copyright Year	2012
Description	Author affiliation: Computer Engineering Department, University of Alcalá, Madrid, Spain (Alvarez, S.; Llorca, D.F.; Sotelo, M.A.; Lorente, A.G.)
Abstract	This paper describes a target detection system on transport infrastructures, based on monocular vision, for applications in the framework of Intelligent Transportation Systems (ITS). Using structured elements of the image, a vanishing point extraction is proposed to obtain an automatic calibration of the camera, without any prior knowledge. This calibration provides an approximate size of the searched targets (vehicles or pedestrians), improving the performance of the detection steps. After that, a background subtraction method, based on GMM and shadow detection algorithms, is used to segment the image. Next a feature extraction, optical flow analysis and clustering methods are used to track the objects. The algorithm is robust to camera jitter, illumination changes and shadows. Therefore it can work indoor and outdoor, in different conditions and scenarios, and independent of the position of the camera. In the paper, we present and discuss the results achieved up to date in real traffic conditions.
Starting Page	61
Ending Page	66
File Size	1080126
Page Count	6
File Format	PDF
ISBN	9781467330640
ISSN	21530009
e-ISBN	9781467330633
e-ISBN	9781467330626
DOI	10.1109/ITSC.2012.6338690
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cameras Calibration Lighting Feature extraction Clustering algorithms Vehicles Image color analysis flock of features Vanishing points Camera calibration Background subtraction shadow detection
Content Type	Text
Resource Type	Article

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