Abstract Falls are a most common problem for old people. They can result in dangerous consequences even death. Many recent works have presented different approaches to detect fall and prevent dangerous outcomes. In this paper, human fall detection from video streams based on a Coupled Hidden Markov Model (CHMM) has been proposed. The CHMM was used to model the motion and static spatial characteristic of human silhouette. The validity of current proposed method was demonstrated with experiments on Le2i database, Weizman database and video from Youtube simulating falls and normal activities. Experimental results showed the superiority of the CHMM for video fall detection.

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