NDLI: A Novel Omni-Directional Vision Sensing Technique for Traffic Surveillance

Content Provider	IEEE Xplore Digital Library
Author	Meng Cao Anh Vu Barth, M.
Copyright Year	2007
Description	Author affiliation: Univ. of California, Riverside (Meng Cao; Anh Vu; Barth, M.)
Abstract	During the past decade, numerous research projects have been carried out in traffic surveillance. A variety of algorithms and techniques have been developed, primarily for observing vehicles from stationary rectilinear cameras mounted near roadways. Various applications include extracting traffic information such as average traffic speed, flow, and density. However, less research has been carried out in observing lane-level vehicle activity operating around specific vehicles in the traffic stream. In this research, we describe a unique orthogonal omni-directional vision system (OODVS) that has been developed to observe lane-level activity surrounding a vehicle, as well as the surrounding roadway geometry. This vision system uses a special catadioptric mirror providing a 360 degree orthogonal view of the environment. It is different from other catadioptric mirror-based omnidirectional vision systems in that it directly provides an orthogonal image without the need of warping a polar-coordinate based image to a perspective view. Based on this unique OODVS, a roadway traffic surveillance system has been designed and implemented. It consists of three major parts: 1) lane-level surrounding vehicle detection; 2) roadway lane information detection; and 3) localized traffic parameter estimation. Further, relationships can be established between the traffic parameters obtained from the probe vehicle with the embedded infrastructure sensors for complementary data fusion. Combined with a global positioning system (GPS) receiver that provides approximately 2-3 meters spatial resolution, this traffic surveillance system can be applied in several lane-level traffic applications such as detailed roadway geometry acquisition, and lane-level navigation tasks.
Starting Page	678
Ending Page	683
File Size	6295436
Page Count	6
File Format	PDF
ISBN	9781424413959
DOI	10.1109/ITSC.2007.4357784
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Surveillance Road vehicles Machine vision Vehicle detection Geometry Cameras Data mining Mirrors Parameter estimation Probes
Content Type	Text
Resource Type	Article

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