Proceedings of the Korean Institute of Intelligent Systems Conference (한국지능시스템학회:학술대회논문집)
- 2003.09a
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- Pages.636-639
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- 2003
Real Time Multiple Vehicle Detection Using Neural Network with Local Orientation Coding and PCA
- Kang, Jeong-Gwan (Dept. of Electrical Eng., Pohang University of Science and Technology) ;
- Oh, Se-Young (Dept. of Electrical Eng., Pohang University of Science and Technology)
- Published : 2003.09.01
Abstract
In this paper, we present a robust method for detecting other vehicles from n forward-looking CCD camera in a moving vehicle. This system uses edge and shape information to detect other vehicles. The algorithm consists of three steps: lane detection, ehicle candidate generation, and vehicle verification. First after detecting a lane from the template matching method, we divide the road into three parts: left lane, front lane, and right lane. Second, we set the region of interest (ROI) using the lane position information and extract a vehicle candidate from the ROI. Third, we use local orientation coding (LOC) edge image of the vehicle candidate as input to a pretrained neural network for vehicle recognition. Experimental results from highway scenes show the robustness and effectiveness of this method.
Keywords