Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

The cost of manual metadata production is high, especially for audiovisual content, where a time-consuming inspection is usually required in order to identify the most appropriate annotations. There is a growing need from digital content industries for solutions capable of automating such a process. In this work we present ACTIVE, a platform for indexing and cataloging audiovisual collections through the automatic recognition of faces and speakers. Adopted algorithms are described and our main contributions on people clustering and caption-based people identification are presented. Results of experiments carried out on a set of TV shows and audio files are reported and analyzed. An overview of the whole architecture is presented as well, with a focus on chosen solutions for making the platform easily extensible (plug-ins) and for distributing CPU-intensive calculations across a network of computers.