Unsupervised discovery of human activities from long-time videos
IET Computer Vision, pp. 522-530, 2015.
In this study, the authors propose a complete framework based on a hierarchical activity model to understand and recognise activities of daily living in unstructured scenes. At each particular time of a long-time video, the framework extracts a set of space-time trajectory features describing the global position of an observed person and ...More
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