Title :
Generating fluent tubes in video synopsis
Author :
Minlong Lu ; Yueming Wang ; Gang Pan
Author_Institution :
Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China
Abstract :
Video synopsis is one of the effective techniques to build a short video representation preserving the essential activities for a long video. Existing methods usually have the problem that a continuous activity (tube) from a single moving object is separated to a few small pieces. In this paper, two schemes are proposed to generate fluent tubes for video synopsis. The Gaussian mixture model and a texture method are combined to detect more compact foreground with shadow removed. The foreground constitutes a set of initial trajectories. A particle filter tracker is used to concatenate two trajectories if they belong to the same foreground activity, which generates more fluent tubes for video synopsis. Experimental results on 4 videos show that our method produces better accuracies and visual effects in video synopsis.
Keywords :
Gaussian processes; image representation; image texture; object detection; particle filtering (numerical methods); video signal processing; Gaussian mixture model; compact foreground detection; fluent tube generation; foreground activity; image texture method; particle filter tracker; short video representation; single moving object; video synopsis; visual effects; Abstracts; Electron tubes; Feature extraction; Histograms; Merging; Streaming media; Trajectory; fluent tube; particle filter; shadow removal; video synopsis;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6638063
