Improving UAV Personalized-Tracking Services by Fusing Visual and Radio Data

This work presents a high precision unmanned aerial vehicle (UAV) communications to detect, track and locate people during moving using real-time dataset. We use a fusion of visual and radio data collected using a UAV, where the UAV is used to provide radio signals to mobile users and to collect visual and wireless datasets simultaneously. During data collection, the UAV is connected to smartphones and laptops, and the UAV is connected to a controller and an edge server through a wireless network. You only look once version4 (YOLOv4), Kalman filter, and long-short term memory (LSTM) algorithms are combined to evaluate the proposed system. YOLOv4-Kalman filter uses video inputs to detect and track people. The detail information of the bounding boxes of the detected persons is integrated with wireless data to locate people in motion. We use a variety of optimization methods, such as batch normalization, error smoothing and DROPOUT to optimize the performances of our proposed system. The proposed model improves the conventional approach by 10 % during localization.