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Dynamic obstacle identification based on global and local features for a driver assistance system
- Dynamic obstacle identification based on global and local features for a driver assistance system
- Woo, JW[Woo, Jeong-Woo]; Lim, YC[Lim, Young-Chul]; Lee, M[Lee, Minho]
- DGIST Authors
- Lim, YC[Lim, Young-Chul]
- Issue Date
- Neural Computing and Applications, 20(7), 925-933
- Article Type
- Automobile Drivers; Driver Assistance System; Dynamic Models; Dynamic Saliency Map; Feature-Based; Feature-Based Model; Feature Extraction; Global Feature; Identification Model; Identification Problem; Image Translation; Local Feature; Local Information; Modified Gist; Moving Obstacles; Object Identification; Obstacle Detection; Obstacle Detectors; Obstacle Identification; Obstacle Recognition; Orientation Feature With Max Pooling; Orientation Features; Saliency Map; Size Variation; Translation (Languages)
- 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. © 2010 Springer-Verlag London Limited.
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