This paper addresses issues in fall detection from videos. Since it has been a broadly accepted intuition that a falling person usually undergoes large physical movement and displacement in a short time interval, the study is thus focused on measuring the intensity and temporal variation of pose change and body motion. The main novelties of this paper include: (a) characterizing pose/motion dynamics based on centroid velocity, head-to-centroid distance, histogram of oriented gradients and optical flow; (b) extracting compact features based on the mean and variance of pose/motion dynamics; (c) detecting human by combining depth information and background mixture models. Experiments have been conducted on an RGB-D video dataset for fall detection. Tests and evaluations show the effectiveness of the proposed method.
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