Detection of surviving humans in destructed environments using a simulated autonomous robot

In this paper, a new approach for detecting surviving humans in destructed environments using a simulated autonomous robot is proposed. The proposed system uses a passive infrared sensor (PIR) in order to detect the existence of living humans and a low-cost camera in order to acquire a snapshot of the scene as needed. Having detected a sign of a living human, the PIR sensor triggers the camera to acquire an image of the scene. The image is fed into a feed-forward neural network, trained to detect the existence of a human body or part of it within an obstructed environment. This approach requires a relatively small number of images to be acquired and processed during the rescue operation. This way, the real-time cost of image processing and data transmission is considerably reduced. The results of the conducted experiments demonstrated that this system has the potential to achieve high performance in detecting living humans in obstructed environments relatively quickly and cost-effectively. The detection accuracy of the system ranged between 79% and 91% depending on a number of factors such as the body position, the light intensity of the scene, and the relative color matching between the body and the surrounding environment.