Title :
Online content-aware video condensation
Author :
Shikun Feng ; Zhen Lei ; Dong Yi ; Li, Stan Z.
Author_Institution :
Lab. of Pattern Recognition, Inst. of Autom., Beijing, China
Abstract :
Explosive growth of surveillance video data presents formidable challenges to its browsing, retrieval and storage. Video synopsis, an innovation proposed by Peleg and his colleagues, is aimed for fast browsing by shortening the video into a synopsis while keeping activities in video captured by a camera. However, the current techniques are offline methods requiring that all the video data be ready for the processing, and are expensive in time and space. In this paper, we propose an online and efficient solution, and its supporting algorithms to overcome the problems. The method adopts an online content-aware approach in a step-wise manner, hence applicable to endless video, with less computational cost. Moreover, we propose a novel tracking method, called sticky tracking, to achieve high-quality visualization. The system can achieve a faster-than-real-time speed with a multi-core CPU implementation. The advantages are demonstrated by extensive experiments with a wide variety of videos. The proposed solution and algorithms could be integrated with surveillance cameras, and impact the way that surveillance videos are recorded.
Keywords :
video signal processing; video surveillance; high-quality visualization; multicore CPU implementation; online content-aware video condensation; sticky tracking method; surveillance camera; surveillance video data; video synopsis; Electron tubes; Games; Optimization; Streaming media; Surveillance; Target tracking; Yarn;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Conference_Location :
Providence, RI
Print_ISBN :
978-1-4673-1226-4
Electronic_ISBN :
1063-6919
DOI :
10.1109/CVPR.2012.6247913