Neuromorphic machine learning for fast object recognition using dynamic vision sensors

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If you step out inadvertently in front of an autonomous, driverless vehicle, you will want to be sure that that it will be capable of making an emergency stop before hitting you. To do so, its reaction times will need to be of the order of a few milliseconds. Relying on conventional, frame-based cameras mounted on the vehicle to respond in this time scale is unrealistic, given the 20 milliseconds or so between each frame. Even more problematic is their poor performance in poor lighting, in heavy rain or snow, or in high contrast sun/shade conditions. Dynamic Vision Sensors (DVS) offer a new camera technology which operates like the human retina, sending out signals from each image pixel as soon as it detects a change in activity, within a few microseconds, in poor light and under high contrast. Using these sensors in applications such as autonomous vehicles requires the development of new Artificial Intelligence, in particular Machine Learning technology. This project aims at developing neuromorphic technology, inspired by how the brain processes information from the retina, which will recognise and respond to visual events as quickly as the DVS detects them, and provide fast object detection and recognition necessary for the most demanding applications.