NDLI: Dynamic obstacle identification based on global and local features for a driver assistance system

Content Provider	Springer Nature Link
Author	Woo, Jeong Woo Lim, Young Chul Lee, Minho
Copyright Year	2010
Abstract	This paper proposes a novel dynamic obstacle recognition system combining global feature with local feature to identify vehicles, pedestrians and unknown backgrounds for a driver assistance system. The proposed system consists of two main procedures: a dynamic obstacle detection model to localize an area containing a moving obstacle, and an obstacle identification model, which is a hybrid of global and local information, for recognizing an obstacle with and without occlusion. A dynamic saliency map is used for localizing an area containing a moving obstacle. For the global feature analysis, we propose a modified GIST using orientation features with MAX pooling, which is robust to translation and size variations of an object. Although the global features are a compact way to represent an object and provide a good accuracy for non-occluded objects, they are sensitive to image translation and occlusion. Thus, a local feature-based identification model is also proposed and combined with the global feature. As such, for the obstacle identification problem, the proposed system mainly follows the global feature-based object identification. If the global feature-based model identifies a candidate area as background, the system verifies the area again using the local feature-based model. As a result, the proposed system is able to provide information on both the appearance of obstacles and the class of an obstacle. Experimental results show that the proposed model can successfully detect obstacle candidates and robustly identify obstacles with and without occlusion.
Starting Page	925
Ending Page	933
Page Count	9
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	20
Issue Number	7
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-06-13
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Obstacle detection Obstacle identification Dynamic saliency map Modified GIST Orientation feature with MAX pooling Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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