This paper presents an artificially intelligent system for automatically detecting unsafe events on stairs from video data. The system progressively applies computer vision and machine learning algorithms to process raw video data and classify stair traversals as normal (safe) or anomalous (unsafe). Experimental results from five test subjects demonstrate that the system can detect 93% of anomalous events with a low false positive rate. Possible limitations of the system and potential improvements are discussed.
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