Neuromorphic machine learning for fast object recognition using dynamic vision sensors

如果你无意中走到一辆自动驾驶汽车前面，你会希望确保它能够在撞到你之前紧急停车。要做到这一点，它的反应时间需要在几毫秒左右。考虑到每帧之间大约有20毫秒的时间间隔，依靠安装在车辆上的传统的基于帧的摄像头来响应这个时间尺度是不现实的。更有问题的是，它们在光线不足、大雨或大雪、或高对比度的阳光/阴影条件下表现不佳。动态视觉传感器（DVS）提供了一种新的摄像技术，它像人类视网膜一样工作，一旦检测到活动的变化，就会在几微秒内从每个图像像素发出信号，在弱光和高对比度下。在自动驾驶汽车等应用中使用这些传感器需要开发新的人工智能，特别是机器学习技术。该项目旨在开发神经形态技术，灵感来自大脑如何处理来自视网膜的信息，这将识别和响应视觉事件的速度与DVS检测到的速度一样快，并为最苛刻的应用提供快速的目标检测和识别。

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.