Concept detection and face pose estimation using lightweight convolutional neural networks for steering drone video shooting

Unmanned Aerial Vehicles, also known as drones, are becoming increasingly popular for video shooting tasks since they are capable of capturing spectacular aerial shots. Deep learning techniques, such as Convolutional Neural Networks (CNNs), can be utilized to assist various aspects of the flying and the shooting process allowing one human to operate one or more drones at once. However, using deep learning techniques on drones is not straightforward since computational power and memory constraints exist. In this work, a quantization-based method for learning lightweight convolutional networks is proposed. The ability of the proposed approach to significantly reduce the model size and increase both the feed-forward speed and the accuracy is demonstrated on two different drone-related tasks, i.e., human concept detection and face pose estimation.