In the last couple of years the machine vision models have seen significant improvements. Based on development of deep convolutional neural networks, we are able to achieve classification accuracy in excess of 95\%. In the meanwhile the amount of available video is increasing rapidly with manual analysis being too slow.
In scientific literature, where the improvements are presented, the authors usually focus on tehnical aspects of quality and present them separately from the pipeline, where they are integrated in a real world scenarios. This is not enough to understand how it works in practial cases.
The aim of this thesis is to develop a web application that integrated SOTA (state of the art) machine vision models and demonstrates a practical application. The purpose of the application is to analyse videos and build a social network graph.
For this purpose, we implemented a pipeline that preforms the analysis and integrated it into the web application. The pipeline and application were tested on open-source datasets, where it achieved very good results. In addition to that the application was also tested on more common everyday videos.
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