Ignacio Martinez-Alpiste, Gelayol Golcarenarenji, Qi Wang and Jose Maria Alcaraz-Calero
|Conference||2020 European Conference on Networks and Communications (EuCNC)|
|Track||Vertical Applications and Internet of Things|
|Core||48% Acceptance Ratio|
|Number of pages||5|
|Early online date||21st of September|
Human Search and Rescue (SAR) tasks are mission-critical and take place in the wild, and thus solutions require timely and accurate human detection on a highly portable platform. This paper proposes a novel lightweight and practical SAR system that meets those demanding requirements by running optimised machine learning in a smartphone, interoperable with Unmanned Aerial Vehicles (UAV) that provides live video feed. In particular, the proposed approach significantly extends a standard machine learning algorithm to achieve adaptive object recognition in response to changing altitudes to accelerate the speed of finding missing people and eliminate redundant computing. Our approach achieved 91.02% of accuracy and real-time speed on a smartphone that hosts the machine learning platform and the new algorithm. This proposed system is highly portable, cost-effective, fast with high accuracy suitable for UAV applications.