Content aware video manipulation (CAVM) is a method for the analysis and recomposition of video footage, by means of content analysis and adaptive video warping. One main motivation of CAVM is ''video retargeting'', a process that visually alters an existing video while considering the relative importance of its various regions. CAVM video retargeting aims at preserving the viewers' experience by maintaining the information content of important regions in the frame, while altering the video dimensions. Other applications include commercial real-estate allocations, time and space content summary, and content deletion (in both time and spatial domain). In this paper we introduce an efficient algorithm for the implementation of CAVM. It consists of two stages. First, the video is analyzed to detect the importance of each pixel in the frame, based on local saliency, motion detection and object detectors. Then, a transformation manipulates the video content according to the aforementioned analysis and application dependent constraints. The visual performance of the proposed algorithm is demonstrated on a variety of video sequences, and compared to the state-of-the-art in image retargeting.
In 2011 Guttmann et al. REF developed a content aware video manipulation method (CAVM) to visually alter a video with respect to the relative importance of various regions.
Content aware video manipulation
COMPUTER VISION AND IMAGE UNDERSTANDING
Computer Science