http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Ego-Motion 보정기법을 적용한 쿼드로터의 화재 감지 알고리즘
이영완(Young-Wan Lee),김진황(Jin-Hwang Kim),오정주(Jeong-Ju Oh),김학일(Hakil Kim) 제어로봇시스템학회 2015 제어·로봇·시스템학회 논문지 Vol.21 No.1
A conventional fire detection has been developed based on images captured from a fixed camera. However, It is difficult to apply current algorithms to a flying Quad-rotor to detect fire. To solve this problem, we propose that the fire detection algorithm can be modified for Quad-rotor using Ego-motion compensation. The proposed fire detection algorithm consists of color detection, motion detection, and fire determination using a randomness test. Color detection and randomness test are adapted similarly from an existing algorithm. However, Ego-motion compensation is adapted on motion detection for compensating the degree of Quad-rotor"s motion using Planar Projective Transformation based on Optical Flow, RANSAC Algorithm, and Homography. By adapting Ego-motion compensation on the motion detection step, it has been proven that the proposed algorithm has been able to detect fires 83% of the time in hovering mode.
표면 벡터를 이용한 스테레오 비전에서의 물체 검출 검증
우태강(Tae-Kang Woo),김희은(Hui-eun Kim),이영완(Young-Wan Lee),김학일(Hakil Kim) 제어로봇시스템학회 2018 제어·로봇·시스템학회 논문지 Vol.24 No.10
The purpose of this paper is to improve the accuracy of object detection by validating the objects detected using surface vectors in stereo vision for object detection in autonomous vehicles or robots. Conventional stereo vision algorithms have a problem of increasing disparity errors between two images by simplifying correspondence matching processes for the purpose of real-time processing. This paper proposes a method to correct the detection result using the surface vector to reduce the false positives due to erroneous disparity images. The proposed method uses Stixel to reduce computation cost when searching for objects in the disparity image, calculates the surface vectors to minimize the false positives of objects due to degradation in disparity image, evaluates the disparity confidence, and then verifies the object. The surface vector is locally computed from the stixel-based ROI to maintain real-time processing. Experiments were carried out using real environment images to verify the performance of the proposed method. Its accuracy was improved by more than 7% compared to the existing methods and the processing time of the surface vector module was less than 6 ms in a PC platform.