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In this paper, we deal with the problem of categorizing different underwater habitat types. Previous works on solving this categorization problem are mostly based on the analysis of underwater images. In our work, we design a system capable of categorizing underwater habitats based on underwater video content analysis since the temporally correlated information may make contribution to the categorization...
This paper presents a wildfire smoke detection method based on a spatiotemporal bag-of-features (BoF) and a random forest classifier. First, candidate blocks are detected using key-frame differences and non-parametric color models to reduce the computation time. Subsequently, spatiotemporal three-dimensional (3D) volumes are built by combining the candidate blocks in the current key-frame and the...
Recent work shows how to use local spatio-temporal features to learn models of realistic human actions from video. However, existing methods typically rely on a predefined spatial binning of the local descriptors to impose spatial information beyond a pure “bag-of-words” model, and thus may fail to capture the most informative space-time relationships. We propose to learn the shapes of space-time...
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