NDLI: Robust detection of preceding vehicles in crowded traffic conditions

Content Provider	Springer Nature Link
Author	Lee, B. Kim, G.
Copyright Year	2012
Abstract	This paper introduces an adaptive scheme for robustly detecting multiple preceding vehicles in crowded traffic conditions. The scheme focuses on issues frequently observed in the interpretation of traffic scenes recorded by cameras installed in vehicles: stable extraction of features and accurate classification in spite of the vehicle’s constant vibrations and dynamic changes in the distance between vehicles. To address these issues, we introduce the concept of integral features and a method of utilizing the scene geometry information. Each of the simple attributes, such as edges, shadows, and symmetry, is compiled in the window confined by the scene geometry to improve the expressiveness and robustness of the extracted features. The scene geometry information that is then estimated from the perspective view is extensively utilized in constituent system components, including not only feature extraction/integration but also neural network-based classification and distance-adaptive clustering. In addition, employing the Kalman filter along with a confidence measure makes the detection and tracking of potential vehicles robust. Experimental results prove that the system employing the proposed scheme detects and tracks multiple vehicles more effectively, even in crowded traffic conditions, with a lower rate of false positives.
Starting Page	671
Ending Page	678
Page Count	8
File Format	PDF
ISSN	12299138
Journal	International Journal of Automotive Technology
Volume Number	13
Issue Number	4
e-ISSN	19763832
Language	English
Publisher	The Korean Society of Automotive Engineers
Publisher Date	2012-05-25
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Preceding vehicle detection Crowded traffic conditions Integral features Vehicle tracking Automotive Engineering
Content Type	Text
Resource Type	Article
Subject	Automotive Engineering

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