Drone-based Autonomous Human Identification for Search and Rescue Missions in Real-time

Drones can be very useful in search and rescue missions primarily due to the aerial imaging capability. Drones can assist ground teams searching for a missing person, and law enforcement officers in crowd control. But there are instances where the observer on the ground control station misses the subtle information on the video feed coming from the drone. In fact, it is a very difficult job for the observer to be so vigilant in viewing a lot of images looking for a sign of a human in those images. This paper presents a drone-based human identification system to make search and rescue missions more effective. The drone, once detects signs of human presence in real-time aerial videos, autonomously navigates towards the suspicious location to get a better view to verify human presence. The drone has been made fully autonomous with operator override as an option. It processes images and sends selected frames for the operator for verification and issuance of instructions for follow-up actions. A custom built Tensorflow neural network is used as the object detector, which processes images in real-time and report human objects if detected on the ground. A single board computer, while running the real-time image processing, reads GPS location also and generates flight commands to the flight controller for autonomous navigation.