SightBit – An AI-Powered Monitoring, Prediction, and Prevention System for Aquatic Sites

Drowning is a leading cause of injury-related death in children. In 2017, drowning claimed the lives of almost 1000 US children younger than 20 years. Anyone can have a water-related accident — even children who know how to swim. A number of strategies are available to prevent these tragedies. Homeowners and pool operators are turning to artificial intelligence for an extra layer of safety to prevent drowning in backyard and public pools. The detection systems, which use submerged cameras and a form of AI known as computer vision, analyze live videos of swimmers and send alerts if they spot a person who appears to be drowning.

SightBit is an artifical intelligence startup using deep learning and computer vision to save lives. It develops a system using image recognition combined with deep learning capability. This visual intelligence technology provides a current picture from the field, allowing for analysis of the items appearing in the photograph, including geography, geological conclusions about the surface, ground, objects, waves, rip current patterns, and their interaction. Draw conclusions, evaluate details, and make judgments, using computerized algorithms and operating conventional devices for real-time alerts. It enhances standard off-the-shelf cameras with software based on deep learning and computer vision technology, with convolutional neural networks for object detection. The system is trained on millions of photo stills to help detect and de-escalate dangerous situations. It develops an AI-Powered prediction and prevention system for the aquatic context: constantly monitoring human-water interfaces, providing earliest possible warnings, ultimately enabling quick response.

Why SightBit

SightBit instantly identifies and alerts to danger to swimmers. Alerts allow human lifeguards and/or aquatic agencies to respond immediately and save lives. The company instantly identifies and alerts to danger to swimmers. Alerts allow human lifeguards and/or aquatic agencies to respond immediately and save lives. SightBit offers software-only subscriptions, in which it provides the processing unit and software. The company prefers to provide its system with cloud computing resources.

SightBit instantaneously identifies and alerts about danger to people in the water. The rapid identification of people in distress allows rescue forces to respond immediately and save lives. It constantly monitors various bridges, locks, quays and automatically alerts concerned management and emergency services in case of any adverse incidents. The company will utilize autonomous devices to enhance monitoring capabilities and also provide real time inputs to special autonomous devices so as to enable accurate search and rescue operations.

SighBit’s Danger Detectors

Sightbit has developed image detection to help spot dangers to aid lifeguards in their work. The system of danger detectors enables lifeguards to keep tabs on a computer monitor that flags potential safety concerns while they scan the beach. Sightbit has developed models based on convolutional neural networks and image detection to provide lifeguards views of potential dangers. Its deep learning and proprietary algorithms have enabled it to identify children alone as well as clusters of people. This allows its system to flag children who have strayed from the pack. The system also harnesses optical flow algorithms to detect dangerous rip currents in the ocean for helping lifeguards keep people out of those zones.  These algorithms make it possible to identify the speed of every object in an image, using partial differential equations to calculate acceleration vectors of every voxel in the image. Lifeguards can get updates on ocean conditions so when they start work they have a sense of hazards present that day.

The Formidable Leader

Netanel Eliav is the Founder and Chief Executive Officer of SightBit. Having strong and proven business and management skills, as well as a relentless Computer Science knowledge, he started working and serving for big organizations like Strauss, Intel, and the Israeli Prime Minister Office as Product Manager and Technology Specialist, gaining along the way more skills including deep learning, computer vision, cyber security, and product design.

Mr. Eliav earned a B.A. in Economics and M.B.A. from Ben-Gurion University.

“We are here to revolutionize aquatic sites monitoring by harnessing artificial intelligence and deep learning for computer vision.”