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In automatic video summarization, visual summary is constructed typically based on the analysis of low-level features with little consideration of video semantics. However, the contextual and semantic information of a video is marginally related to low-level features in practice although they are useful to compute visual similarity between frames. Therefore, we propose a novel video summarization...
We present an automatic and efficient method to extract spatio-temporal human volumes from video, which combines top-down model-based and bottom-up appearance-based approaches. From the top-down perspective, our algorithm applies shape priors probabilistically to candidate image regions obtained by pedestrian detection, and provides accurate estimates of the human body areas which serve as important...
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