NDLI: Illumination aware MCMC Particle Filter for long-term outdoor multi-object simultaneous tracking and classification

Content Provider	IEEE Xplore Digital Library
Author	Bardet, Francois Chateau, Thierry Ramadasan, Datta
Copyright Year	2009
Description	Author affiliation: LASMEA, Université Blaise Pascal, 24 avenue des Landais, F-63177 Aubière cedex, FRANCE (Bardet, Francois; Chateau, Thierry; Ramadasan, Datta)
Abstract	This paper addresses real-time automatic visual tracking, labeling and classification of a variable number of objects such as pedestrians or/and vehicles, under time-varying illumination conditions. The illumination and multi-object configuration are jointly tracked through a Markov Chain Monte-Carlo Particle Filter (MCMC PF). The measurement is provided by a static camera, associated to a basic foreground / background segmentation. As a first contribution, we propose in this paper to jointly track the light source within the Particle Filter, considering it as an additionnal object. Illumination-dependant shadows cast by objects are modeled and treated as foreground, thus avoiding the difficult task of shadow segmentation. As a second contribution, we estimate object category as a random variable also tracked within the Particle Filter, thus unifying object tracking and classification into a single process. Real time tracking results are shown and discussed on sequences involving various categories of users such as pedestrians, cars, light trucks and heavy trucks.
Starting Page	1623
Ending Page	1630
File Size	1100678
Page Count	8
File Format	PDF
ISBN	9781424444205
ISSN	15505499
DOI	10.1109/ICCV.2009.5459367
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-29
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lighting Particle filters Particle tracking Light sources Surveillance Target tracking Vehicles Image segmentation Computer vision Labeling
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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