Study on human detection system using deep neural network and alternative learning for autonomous flying drones

An alternative learning and its application to construct an overviewing human detection system (OHDES-V2) of flying drone for emergency rescue and investigation is presented in this paper. In this system, a deep neural network and alternative learning are used key techniques for object recognition from a free viewpoint. Simple appearance-based characteristics is determined from captured images, and the system uses a deep neural network to automatically classify human body, automobiles and so forth. The proposed system shows that several objects can be recognized from a bird’s-eye view. Experimental results show that the system can effectively recognize four types of objects and walking persons with accuraces of 98.5% and 97.12%, respectively. © 2019 The Institute of Electrical Engineers of Japan.