NDLI: Predicting high resolution image edges with a generic, adaptive, 3-D vehicle model

Content Provider	IEEE Xplore Digital Library
Author	Leotta, M.J. Mundy, J.L.
Copyright Year	2009
Description	Author affiliation: Brown Univ., Provdence, RI, USA (Leotta, M.J.; Mundy, J.L.)
Abstract	In traffic surveillance applications a good prior model of vehicle shape and appearance is becoming increasingly more important for tracking, shape recovery, and recognition from video. The usefulness of 2-d vehicle models is limited to a fixed viewing direction; 3-d models are nearly always more suitable. Existing 3-d vehicle models are either generic but far too simple to utilize high resolution imagery, or far too complex and limited to specific vehicle instances. This paper presents a deformable vehicle model that spans these two extremes. The model is constructed with a multi-resolution approach to fit various image resolutions. At each resolution, a small number of parameters controls the deformation to accurately represent a wide variety of passenger vehicles. The parameters control both 3-d shape and appearance of parts that deform in the 2-d manifold of the vehicle surface. These parts are regions representing windows, headlights, taillights, etc. The combination of part boundaries and surface occluding contours account for the most consistent edges observed in images of vehicles. It is shown that the model parameters can be recovered by fitting the deformable model to real images of vehicles.
Starting Page	1311
Ending Page	1318
File Size	6630892
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206738
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image resolution Vehicles Predictive models
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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