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

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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.

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Acknowledgments

This research was supported by the Daegu Gyeongbuk Institute of Science and Technology (DGIST) and the IT R&D program of MKE/KETI. [10033776, Core technology development of large-scale, intelligent and cooperative surveillance system]. Also, this research was supported by the Converging Research Center Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2009-0082262)(50%).

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu, 702-701, Korea

    Jeong-Woo Woo & Minho Lee

  2. Division of Advanced Industrial Science and Technology, Daegu Gyeongbuk Institute of Science and Technology, 711 Hosan-Dong, Dalseo-Gu, Taegu, 704-230, Korea

    Young-Chul Lim

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  1. Jeong-Woo Woo
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  2. Young-Chul Lim
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  3. Minho Lee
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Correspondence to Minho Lee.

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Woo, JW., Lim, YC. & Lee, M. Dynamic obstacle identification based on global and local features for a driver assistance system. Neural Comput & Applic 20, 925–933 (2011). https://doi.org/10.1007/s00521-010-0401-9

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  • DOI: https://doi.org/10.1007/s00521-010-0401-9

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