Video Intelligence once considered the exclusive intel of humans has now taken a turn with the advances in Artificial intelligence algorithms and the increasing processing power of AI Gateways.

Intelligence and data-driven decisions based on video and camera are now of prime importance finding its way into applications like Smart Parking, Retail footfall analytics, Traffic Management, and security surveillance. Insights from video and images have the capability of providing you vast amounts of data – both for predictive analytics and historical analytics.

Complementing the infrastructure of multiple cameras present across buildings, airports, retail stores, and other zones, there is now a need for an intelligent gateway to collect the images at a high resolution with connectivity options while also being capable of taking the decision on the edge. With Corazon-AI, we present an efficient Multi-Channel – AI Video Analytics gateway. Through the design of an 8-channel Xilinx Video Codec Unit (VCU) + CNN inference deployed on Corazon-AI, the gateway serves as a low-power heterogeneous compute platform enabling edge computing.

Through the above video integrating our demo applications, we intend to demonstrate the capability and performance of the VCU (Video Codec Unit) available as a hard block IP. The AI Inference Engine and Deep Learning Processing Unit (DPU) are implemented in the PL (programmable logic) side of the device.

Video data from eight RTSP streams from the 8 Cameras are processed alongside high-speed deep Learning analytics performed on each video stream at the edge on Corazon-AI. Given below is an architecture of the video streaming and analytics architecture on Corazon-AI running different models on each of the cameras.


About The Author

Muhammad Bilal

I am highly skilled and motivated individual with a Master's degree in Computer Science. I have extensive experience in technical writing and a deep understanding of SEO practices.

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