Industry Expert Blogs

The growing complexity of imaging pipelines continues to surprise us. Far from the simplified view of a camera connecting directly to an AI stage for analysis, now a series of computer vision (CV) transformations are required to condition images (possibly from multiple cameras) before they are ready for AI processing. This need is especially apparent in surveillance camera applications.

Surveillance cameras demand

Surveillance camera growth continues for general applications in security and cost management. Home safety is an obvious example. More generally protecting small and large stores from recent trends in flash robbery is growing in importance, both for business safety and to minimize costs and inconvenience for us consumers. Drones mounted with multi-spectral cameras (color vision fused with infra-red vision for example), through their wide-ranging mobility can seek out early wildfire signs, alerting fire crews to suppress spot fires before they run out of control. Similarly, camera enabled drones can survey crops to monitor irrigation, fertilization and pest management.

All these applications require high quality yet compact vision solutions, commonly across multiple cameras and with minimal power budgets. Before AI takes over, important CV conditioning functions must pre-process camera images and videos to the latest requirements for surveillance imaging quality, through transformations which can’t be managed efficiently by either a CPU or an NPU.

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