From passive video capturing devices to fully autonomous vision systems, recent advances in embedded vision triggered an evolution. Self-driving cars, drones, or autonomous guided robots require real-time parallel processing, low-latency, and in some cases low current consumption. 

Building autonomous vision systems grows increasingly challenging, requiring multi-disciplinary expertise in optics, image sensors, computer vision and deep learning. Selecting the right development platform and design methodology is crucial for a successful implementation. Let us introduce you to a solution - the multi-camera methodology.

Multiple camera modules provide surround view. Sensor fusion improves the overall vision system, while artificial intelligence and machine learning drive tremendous improvements for recognition and learning tasks in autonomous vision systems.