Упорядочивание данных в системах видеонаблюдения на основе технологий глубокого обучения
The task of organizing information in video surveillance systems is implemented by grouping the video tracks, which contain identical faces. We examine aggregation methods for the features of individual frames extracted using deep convolutional neural networks. The tracks with identical faces are grouped based on known face verification algorithms and clustering methods. Experimental study on the YouTubeFaces dataset demonstrates results of combining frame features in order to obtain a descriptor of video track. It is shown that the most accurate method is L2-normalization of average unnormalized features of individual frames of each video track.