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The authors study a flexible framework for semantic analysis of human motion from surveillance video. Successful trajectory estimation and human-body modeling facilitate the semantic analysis of human activities in video sequences. Although human motion is widely investigated, they have extended such research in three aspects. By adding a second camera, not only more reliable behavior analysis is possible, but it also enables to map the ongoing scene events onto a 3D setting to facilitate further semantic analysis. The second contribution is the introduction of a 3D reconstruction scheme for scene understanding. Thirdly, they perform a fast scheme to detect different body parts and generate a fitting skeleton model, without using the explicit assumption of upright body posture.
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