Real-time deep learning on video streams

Deep learning is revolutionizing many domains within computer vision, but doing real-time analysis is challenging. Eran Avidan offers an overview of a novel architecture based on Redis, Docker, and TensorFlow that enables real-time analysis of high-resolution streaming video.

Deep learning has recently become an abundant technology for analyzing video data. However, the increasing resolution and frame rates of videos make real-time analysis a remarkably challenging task. Eran Avidan offers an overview of a novel architecture based on Redis, Docker, and TensorFlow that enables real-time analysis of high-resolution streaming video. The solution can serve advanced deep learning algorithms at subsecond rates and appears fully synchronous to the user despite containing an asynchronous backend. Eran offers a demo using visual inspection and shares results that highlight the solution’s applicability to real-time neural network processing of videos. The approach is generalizable and can be applied to diverse domains that require video analytics.