Ubicept

2023年3月10日,

Ubicept希望世界上一半的攝像機看到不同計算機視覺可以更快和更好的如果我們還是跳過的概念框架,而是直接從相機對數據流進行分析。至少,這是最新的理論構想旋轉麻省理工學院媒體實驗室,Ubicept操作下。大多數計算機視覺應用程序相同的方式工作:相機拍出來的圖片(或一係列快速的圖片、視頻)的情況。這些畫麵仍然是傳遞給電腦,然後是分析圖像中找出是什麼。聽起來相當簡單。但是有一個問題:範例假設創造畫麵仍然是一個好主意。人類習慣於看到攝影和視頻,這似乎是合理的。電腦不在乎,然而,Ubicept相信它能使計算機視覺更好和更可靠的忽視的概念框架。公司本身是一個其創始人之間的協作。塞巴斯蒂安·鮑爾是該公司的首席執行官和威斯康辛大學博士後,他致力於激光雷達係統。 Tristan Swedish is now Ubicept’s CTO. Before that, he was a research assistant and a master’s and Ph.D. student at the MIT Media Lab for eight years. “There are 45 billion cameras in the world, and most of them are creating images and video that aren’t really being looked at by a human,” Bauer explained. “These cameras are mostly for perception, for systems to make decisions based on that perception. Think about autonomous driving, for example, as a system where it is about pedestrian recognition. There are all these studies coming out that show that pedestrian detection works great in bright daylight but particularly badly in low light. Other examples are cameras for industrial sorting, inspection and quality assurance. All these cameras are being used for automated decision-making. In sufficiently lit rooms or in daylight, they work well. But in low light, especially in connection with fast motion, problems come up.” The company’s solution is to bypass the “still frame” as the source of truth for computer vision and instead measure the individual photons that hit an imaging sensor directly. That can be done with a single-photon avalanche diode array (or SPAD array, among friends). This raw stream of data can then be fed into a field-programmable gate array (FPGA, a type of super-specialized processor) and further analyzed by computer vision algorithms. The newly founded company demonstrated its tech at CES in Las Vegas in January, and it has some pretty bold plans for the future of computer vision. “Our vision is to have technology on at least 10% of cameras in the next five years, and in at least 50% of cameras in the next 10 years,” Bauer projected. “When you detect each individual photon with a very high time resolution, you’re doing the best that nature allows you to do. And you see the benefits, like the high-quality videos on our webpage, which are just blowing everything else out of the water.” TechCrunch saw the technology in action at a recent demonstration in Boston and wanted to explore how the tech works and what the implications are for computer vision and AI applications. A new form of seeing Digital cameras generally work by grabbing a single-frame exposure by “counting” the number of photons that hit each of the sensor pixels over a certain period of time. At the end of the time period, all of those photons are multiplied together, and you have a still photograph. If nothing in the image moves, that works great, but the “if nothing moves” thing is a pretty big caveat, especially when it comes to computer vision. It turns out that when you are trying to use cameras to make decisions, everything moves all the time. Of course, with the raw data, the company is still able to combine the stream of photons into frames, which creates beautifully crisp video without motion blur. Perhaps more excitingly, dispensing with the idea of frames means that the Ubicept team was able to take the raw data and analyze it directly. Here’s a sample video of the dramatic difference that can make in practice: