It is a widely known fact in the tech world that drones are flown at high altitudes, and they cannot yet fly autonomously in complex environments, like dense forests. However, thanks to latest advancements in artificial intelligence and computer vision, today drones can maneuver indoors, around difficult to reach nooks, bends and even dense forests too. Well, recently drones again hit the headlines, owing to their new ability to help people, hikers lost in woods. In their paper published in the journal Nature Machine Intelligence, researchers, David Schedl, Indrajit Kurmi and Oliver Bimber, from Johannes Kepler University, share how artificial intelligence to improve thermal imaging camera searches for people lost in woods. When hikers, trekkers or commoners are lost in woods, rescue team rely on binoculars, and thermal imagers installed on camera and in chopper sensors, to find the missing.

A team of researchers have developed a new algorithm that could help search and rescue teams locate people lost at sea using ocean currents, wind speed, and wave direction. The project was a joint effort from scientists at MIT, the Swiss Federal Institute of Technology (ETH), the Woods Hole Oceanographic Institution (WHOI), and Virginia Tech, who tested their method using human manikins in the ocean off the coast of Martha's Vineyard. Unlike current search and rescue models--which also use data about ocean currents and wind to calculate the likely location of a missing person by simulating one single linear path--the team's new system is focused on identifying multiple points of'attraction' in the ocean, which can sometimes change dramatically over time. Using a system they called Transient Attracting Profiles (TRAPS), the team tracks these attraction points, which they behave like'moving magnets' pulling people in the water toward them. Instead of mapping out a single, linear path, the TRAPS model identifies many different attraction points, or'traps,' in the ocean that will likely have pulled a person in multiple directions as they drift through the waters.

A team of researchers at China University of Geosciences and Wuhan WXYZ Technologies in China has recently proposed a new machine learning-based technique to locate people's eyes in images of their faces. This technique, presented in a paper published in Elsevier's journal Neurocomputing, could have several useful applications. For example, it could be used to detect drowsiness in people who are driving a car or performing tasks that require a certain degree of alertness and attention. Drowsiness can greatly impair people's decision-making skills, as well as their attention and memory. Drowsiness while driving or completing an important task can lead to a significant decline in efficiency, and in some cases, even cause life-threatening accidents.