http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
A Comparative Study of Foreground Detection using Gaussian Mixture Models- Novice to Novel
Ajmal Shahbaz,Laksono Kurnianggoro,Kang-Hyun Jo 제어로봇시스템학회 2016 제어로봇시스템학회 국제학술대회 논문집 Vol.2016 No.10
Foreground detection is the classical computer vision task of segmenting out motion information from a particular scene. Foreground detection using Gaussian Mixture Models (GMM) is the famous choice. Since first time proposed, many researchers tried to improve GMM. This paper focuses on the comparative evaluation of three most famous improvements in the algorithm. The improved methods are compared both qualitatively and quantitatively using standard datasets available online.
Dense Optical Flow in Stabilized Scenes for Moving Object Detection from a Moving Camera
Laksono Kurnianggoro,Ajmal Shahbaz,Kang-Hyun Jo 제어로봇시스템학회 2016 제어로봇시스템학회 국제학술대회 논문집 Vol.2016 No.10
This paper proposes a method for detecting moving objects appeared in video captured by a moving camera. The proposed method relies on dense optical flow to differentiate moving objects from static background. Whenever video taken from a static camera is used, the dense optical flow itself is sufficient to determine the moving object in the scenes. However, in a non-static camera, all pixels are moving making which lead to incapability of optical flow to differentiate the moving objects from the static background. In order to solve this problem, a stabilization method is incorporated by the mean of global motion extraction, which can be done by analyzing the homography transformation between two consequtive frames. Finally, by applying a threshold on the dense optical flow, the region of moving object is acquired. The proposed method has been evaluated in the experiments and produce satisfying results with 98% accuracy.