IEEE Signal Processing Letters · 2015 · 19 citations · 17 references
ComputingComputer ScienceComputer VisionDigital ImagingMachine VisionDigital SurveillanceMotion AnalysisVideo AnalysisVisual TrackingCamerasCondensation RateObjectsSurveillance CamerasVideo CarvingVideo Object TrackingVisual Object Tracking
With a growth of surveillance cameras, the amount of captured videos expands. Manually analyzing and retrieving surveillance video is labor intensive and expensive. It would be much more convenient to generate a video digest, with which we can view the video in a fast and motion-preserving way. In this paper, we propose a novel video synopsis approach to generate condensed video, which uses an object tracking method for extracting important objects. This method will generate video tubes and a seam carving method to condense the original video. Experimental results demonstrate that our proposed method can achieve a high condensation rate while preserving all the important objects of interest. Therefore, this approach can enable users to view the surveillance video with great efficiency.
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Online Object Tracking: A Benchmark
Yi Wu, Jongwoo Lim, Ming–Hsuan Yang · 2013 · 4.1K citations
Seam carving for content-aware image resizing
Shai Avidan, Ariel Shamir · 2007 · 1.4K citations
Seam carving for content-aware image resizing
Shai Avidan, Ariel Shamir · ACM Transactions on Graphics · 2007 · 1.1K citations
Improved seam carving for video retargeting
Michael Rubinstein, Ariel Shamir, Shai Avidan · ACM Transactions on Graphics · 2008 · 745 citations