Making Pools Safer with Data Annotation
The United States’ Centre for Disease Control and Prevention estimates that around 10 Americans die from unintentional drowning every day. Tragically, many of these accidents involve young children and residential pools. Un
Making Pools Safer with Data Annotation
The United States’ Centre for Disease Control and Prevention estimates that around 10 Americans die from unintentional drowning every day. Tragically, many of these accidents involve young children and residential pools. Unsupervised and unfenced pools can lead to drowning incidents occurring without the knowledge of parents or guardians.
More and more focus is being turned towards this issue as the popularity of home pools grows. AI developers are now stepping in with systems that have the potential to dramatically improve pool safety and save lives.
This blog will address some of the key issues that need to be solved by AI companies in this sector. Machine learning is creating computer vision models that can address these problems. In order to progress this important technology developers are looking to collaborate with image annotation providers, like Keymakr, to construct high quality training datasets.
Pools are usually protected with fences and covers that prevent access. However, these measures are only useful when the pool is not being used, in many homes pools are regularly left open and unattended. An integrated system that watches the pool, above and below water, could be much more useful for consumers. Simple movement detections systems can alert users if somebody is in the pool.
However, these kinds of alarms need to be activated and deactivated when entering and leaving the pool. This can easily be forgotten, leaving the pool area unguarded.
A fully automated pool safety system must be able to recognise when somebody is entering the pool at any time. It must be able to distinguish between the shape of humans and other objects, such as pool toys or debris. Crucially, smart pool safety ecosystems must be able to recognize signs of distress and drowning without constantly raising the alarm due to false detections. The only way to accomplish this kind of integrated, functional system is with the help of computer vision based AI.
AI based solutions
Images and video can be used to train pool safety AIs to constantly monitor the water. Rather than sensing water movement, AI back cameras can “see” who is in the water and interpret their behaviour. For example, if a child is motionless in the water, with their head submerged, the system will trigger an al