Gaussian Mixture Model for summarization of surveillance videos
Sujatha Canavoy Narahari, Uma Mudenagudi · 2015
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.