Cameras and other equipment used in video surveillance and machine vision perform a variety of different tasks, such as Image Signal Processing (ISP), video transport, format conversion, compression, and analytics. Because of the frequent technology improvements to camera sensors, the trend to replace analog cameras with smart Internet Protocol cameras, and the advancement of artificial learning deep learning-based video analytics, FPGAs exceed many of the key requirements needed for vision-based systems:
- High performance per watt
Combined with Intel® CPUs, FPGA-based accelerator solutions are now available for the architectural redesign of next-generation vision-based equipment.
Government, municipalities, financial institutions, and businesses are driving new uses for video surveillance technologies beyond crime prevention or security into new applications, such as video-based asset management, risk mitigation, retail customer analytics, and crowd safety.
The challenge for camera manufacturers is where to insert the "smart/analytics" functions within the end-to-end video solution. Artificial intelligence technology for video processing has advanced to the point where it has become one of the defacto requirements for next-generation vision processing systems but constant innovation by academia and industry makes it challenging to incorporate fixed functionality into ASICs or ASSPs. Contact us to discuss how to design vision solutions to achieve the right balance of performance, power, flexibility, total cost of ownership (TCO), and low latency to extract valuable or actionable insights from raw video data.
Machine vision (MV) uses a combination of high-speed cameras and computers to perform complex inspection tasks in addition to digital image acquisition and analysis. You can use the resulting data for pattern recognition, object sorting, robotic arm control, and more. Intel FPGAs are ideal for MV cameras, allowing designs to accommodate a wide variety of image sensors as well as MV-specific interfaces. FPGAs can also be used as vision processing accelerators inside the Edge computing platform to harness the power of artificial intelligence deep learning for analysis of the MV data.