Constantly optimising deep learning algorithms yields better video analytics performance, even in complex applications such as facial recognition or in scenarios with variable lighting, angles, postures, expressions, accessories, resolution, etc.
Deep learning, a form of artificial intelligence (AI), holds the potential to enable video analytics to deliver on long-promised, but not often delivered performance. Our AI series continues here with part 2.
Adapting existing hardware
Today, low-cost system-on-chip (SoC) camera components enable deep neural network (DNN) processing for the next generation of intelligent cameras, thus expanding the availability of AI processing to a broader market.
AI software can even add learning capabilities by adapting existing hardware to AI applications
AI software can even add learning capabilities by adapting existing hardware to AI applications. Today’s smartphones include cameras, gyroscopes and accelerometers to provide sufficient data to drive AI applications. Software can adapt existing hardware to transform them into AI devices capable of continuous learning in the field. Inside a video camera, real-time deep learning processing can be used to detect discarded objects, issue loitering alarms and detect people or objects entering a pre-defined field.