Harsh Kumar explains one way the energy industry is using AI and computer vision for security surveillance: a video analytics solution that can be optimized for the functional safety of workers in the loading and unloading zone of an oil and gas offshore rig. Harsh then demonstrates how the same platform can be used for human intrusion detection for high-value assets.
There is an increasing demand for running basic computer vision and deep learning models on edge devices for quick response/alarm/alert generation, privacy, security, etc. The information from the edge is then sent to an on-premises computer and then on to the cloud for further processing, such as tracing the subject and training the AI model. The challenge is to optimize and balance this end-to-end solution. Harsh details the technologies to use and explores challenges system developers may face, from the number of cameras and their resolutions to the number of intelligent gateways to optimize performance, detect, and correct anomalies. Along the way, Harsh dissects the amount of data sent to the cloud or kept on-premises and shows how all these challenges can be addressed by modeling and simulating end-to-end solutions using Intel CoFluent for the IoT.
Harsh Kumar is a business development manager at Intel, where he focuses on system simulation products for the IoT, autonomous cars, the cloud, and memory subsystems.
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