Near-drowning Early Prediction Technique Using Novel Equations (NEPTUNE) for Swimming Pools
This addresses safety concerns for swimmers by enabling early lifeguard alerts, but it appears incremental as it builds on existing statistical image processing and clustering methods.
The paper tackles the problem of early detection of near-drowning incidents in swimming pools by developing NEPTUNE, a technique that uses novel equations to predict such events from video sequences within 1-5 seconds with no false positives.
Safety is a critical aspect in all swimming pools. This paper describes a near drowning early prediction technique using novel equations (NEPTUNE). NEPTUNE uses equations or rules that would be able to detect near drowning using at least 1 but not more than 5 seconds of video sequence with no false positives. The backbone of NEPTUNE encompasses a mix of statistical image processing to merge images for a video sequence followed by K means clustering to extract segments in the merged image and finally a revisit to statistical image processing to derive variables for every segment. These variables would be used by the equations to identify near-drowning. NEPTUNE has the potential to be integrated into a swimming pool camera system that would send an alarm to the lifeguards for early response so that the likelihood of recovery is high.