Deep learning has recently become an abundant technology for analyzing video data. However, the increasing resolution and frame rates of videos makes 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.
Eran Avidan is a senior software engineer in Intel’s Advanced Analytics Department. Eran enjoys everything distributed, from Spark and Kafka to Kubernetes and TensorFlow. He holds an MS in computer science from the Hebrew University of Jerusalem.
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