1000x Faster Camera and Machine Vision with Ordinary Devices
This work addresses speed limitations in camera and machine vision for applications like photography, movies, and visual media, with claims of fundamental revolution, though it appears incremental in combining existing concepts.
The paper tackled the limitation of conventional cameras by introducing vidar, a bit sequence array that records scene radiance using consumer-level CMOS sensors, achieving 1,000x faster capture than traditional cameras, and developed a spiking neural network-based vision system for high-speed object detection and tracking 1,000x faster than human vision.
In digital cameras, we find a major limitation: the image and video form inherited from a film camera obstructs it from capturing the rapidly changing photonic world. Here, we present vidar, a bit sequence array where each bit represents whether the accumulation of photons has reached a threshold, to record and reconstruct the scene radiance at any moment. By employing only consumer-level CMOS sensors and integrated circuits, we have developed a vidar camera that is 1,000x faster than conventional cameras. By treating vidar as spike trains in biological vision, we have further developed a spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1,000x faster than human vision. We demonstrate the utility of the vidar camera and the super vision system in an assistant referee and target pointing system. Our study is expected to fundamentally revolutionize the image and video concepts and related industries, including photography, movies, and visual media, and to unseal a new spiking neural network-enabled speed-free machine vision era.