NDLI: Moving objects detection and recognition using sparse spatial information in urban environments

Content Provider	IEEE Xplore Digital Library
Author	You Li Ruichek, Y.
Copyright Year	2012
Description	Author affiliation: Laboratoire Systèmes et Transports, Université de Technology de Belfort-Montbéliard, Belfort, 90010 France (You Li; Ruichek, Y.)
Abstract	Moving objects detection and recognition around an intelligent vehicle are active research fields. A great number of approaches have been proposed in recent decades. This paper proposes a novel approach based solely on spatial information to solve this problem. Moving objects detection is achieved in conjunction with an egomotion estimation by sparse matched feature points. For objects recognition, we firstly present a method to boost simple spatial information by Kernel Principal Component Analysis (KPCA). Then, two kinds of classifiers (Random Forest and Gradient Boosting Trees) are trained offline to recognize several common categories of moving objects in urban scenarios (vehicle, pedestrian, cyclist, …). Experiments are implemented and the results confirm the effectiveness of the proposed algorithm. Furthermore, a comparison to a previous similar method is performed to verify the enhancement of classification by the advanced spatial features.
Starting Page	1060
Ending Page	1065
File Size	2136801
Page Count	6
File Format	PDF
ISBN	9781467321198
ISSN	19310587
e-ISBN	9781467321181
DOI	10.1109/IVS.2012.6232205
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-03
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Kernel Vehicles Object detection Boosting Intelligent vehicles Urban areas
Content Type	Text
Resource Type	Article

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