학술논문
Gaussian Mixture Model for summarization of surveillance videos
Document Type
Conference
Author
Source
2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2015 Fifth National Conference on. :1-4 Dec, 2015
Subject
Language
Abstract
We propose a method to address the problem of Video Summarization, which aims to generate a summarized video by preserving the salient activities of the input video for a user specified time. We model the motion of a feature points as Gaussian Mixture Model (GMM) to select the key feature points, which in-turn estimate the salient frames. The saliency of feature points depends on the contribution of motion in entire video and user specified time duration of summary. We generate a summarized video keeping chronology of salient frames to avoid the viewing ambiguity for the viewers. We demonstrate the proposed method for different stored surveillance videos and achieve retention ratio as 1 for the closest condensation ratio obtained for stroboscopic approach and also demonstrate the proposed GMM method with interactively selected region of interest (ROI) based results.