Detection of early dangerous state in deep water of indoor swimming pool based on surveillance video

This paper presents a method for early detection of dangerous condition in the deep-water zone of swimming pool based on video surveillance. We propose feature extraction, feature expression and assessment criteria, including a method for evaluating normal swimming speed based on the time series of swimmers, a method for assessing an upright state that is not limited by the camera angle, and the rules for assessing dangerous state. We have collected real-life data from the swimming pool and conducted related experiments. Our method can easily and efficiently detect the swimmer who is in danger at an early stage and provide necessary rescue reminders to lifeguards.