Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

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.

@Article{SSA21,
  author       = {Sassu, A. and Saenz Cogollo, J. and Agelli, M.},
  title        = {Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams},
  journal      = {Sensors},
  number       = {12},
  volume       = {21},
  month        = {june},
  year         = {2021},
  publisher    = {MDPI},
  keywords     = {real-time video analytics, edge computing, deep learning, distributed systems, software framework},
  doi          = {10.3390/s21124045 },
  url          = {https://publications.crs4.it/pubdocs/2021/SSA21},
}