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http://dx.doi.org/10.22156/CS4SMB.2017.7.6.229

Obstacle Detection and Recognition System for Autonomous Driving Vehicle  

Han, Ju-Chan (Department of Computer Science, Chungbuk National University)
Koo, Bon-Cheol (Department of Computer Science, Chungbuk National University)
Cheoi, Kyung-Joo (Department of Computer Science, Chungbuk National University)
Publication Information
Journal of Convergence for Information Technology / v.7, no.6, 2017 , pp. 229-235 More about this Journal
Abstract
In recent years, research has been actively carried out to recognize and recognize objects based on a large amount of data. In this paper, we propose a system that extracts objects that are thought to be obstacles in road driving images and recognizes them by car, man, and motorcycle. The objects were extracted using Optical Flow in consideration of the direction and size of the moving objects. The extracted objects were recognized using Alexnet, one of CNN (Convolutional Neural Network) recognition models. For the experiment, various images on the road were collected and experimented with black box. The result of the experiment showed that the object extraction accuracy was 92% and the object recognition accuracy was 96%.
Keywords
Driving on the road; Optical Flow; CNN; AlexNet; Black Box;
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