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Neuromorphic event-based vision is an emerging paradigm of acquisition and processing of visual information that takes inspiration from the functioning of the human vision system, attempting to recreate Nature's visual information acquisition and processing on VLSI silicon. In contrast to conventional image sensors, event vision sensors do not use a common sampling rate (=frame rate) for all pixels, but each individual pixel autonomously defines the timing of its own sampling points in response to its visual input by reacting to changes of incident light in continuous time. The highly efficient way of acquiring sparse yet relevant data, the high temporal resolution, and the robustness to uncontrolled lighting conditions, are characteristics of the event sensing process that make event vision attractive for numerous applications in industrial, surveillance, IoT, AR/VR, automotive and mobile, often taking the form of machine learning-based at-the-edge visual perception systems that are able to cope with limited resources and achieve a high degree of autonomy. This presentation will give an introduction to event vision technology and highlight a few exemplary use cases.
Alessandro Marchioro, Michael Campbell, Xavi Llopart Cudie