Electronic Weapons: Straight Out Of China And Microsoft

电子武器:来自中国和微软FairMOT系统

Date:2020-05-08 Source:strategypage By:Globalmil Viewed:


May 5, 2020: A team of American (Microsoft) and Chinese (Huazhong University) researchers recently released FairMOT (Fair Multi-Object Tracking), an open-source (free for anyone to use) AI Object Detector system. What this software system does is speed up (30 frames a second) the identification of objects on video that more quickly and precisely follows objects in the video. Civilian uses include security and monitoring hospitals or elder-care facilities to detect potential problems. This also has military uses, the major one being able to make better use of the huge American databases of UAV and ground-based security videos. Currently, all this video overwhelms the ability of troops to extract useful information. Since the 1990s there have been numerous projects undertaken to make better sense of all this data. The proliferation of video cameras on the battlefield, especially UAVs (as well as ground and underwater vehicles) for surveillance or base security has created a huge library of images that show bad guys doing what bad guys do and what they look like while doing it. This can range from moving around carrying weapons, to using those weapons, to the particular driving patterns of people up to no good. These images are a unique resource, and the U.S. is putting together a library of these images. This is similar to older still pictures libraries, which were eventually used by pattern recognition software to let machines examine the millions of images digital photo satellites began producing decades ago. The basic problem was that very soon there were too many pictures for human analysts to examine. Computers had to do much of the work, or else most of the images would go unexamined. This technology was quickly adapted to the kind of combat encountered in Iraq and Afghanistan, and terrorist operations in general.
2020年5月5日:由美国(微软)和中国(华中大学)研究人员组成的团队最近发布了FairMOT(公平的多对象跟踪),这是一种开源(任何人免费使用)的AI Object Detector系统。该软件系统的作用是加快(每秒30帧)对视频中对象的识别,从而更快,更准确地跟踪视频中的对象。民用用途包括安全和监视医院或老年护理设施,以发现潜在问题。这也有军事用途,主要用途是可以更好地利用庞大的美国无人机数据库和地面安全视频。目前,所有这些视频都压垮了部队提取有用信息的能力。自1990年以来,开展了许多项目以更好地理解所有这些数据。战场上摄像机的激增,尤其是用于监视或基础安全的无人机(以及地面和水下机器人)已经创建了一个庞大的图像库,可以显示坏人的行为以及坏人做坏事的样子。这可以从携带武器四处走动,到使用这些武器,对特定驾驶模式的人来说没有用处。这些图片是一种独特的资源,美国正在将这些图片的库放在一起。这类似于较早的静态图片库,最终被模式识别软件用来让机器检查几十年前数字照相卫星开始产生的数百万张图片。基本的问题是很快就有太多的图片供人类分析人员检查。计算机必须完成很多工作,否则大多数图像将无法检查。这项技术迅速适应了伊拉克和阿富汗境内的战斗以及一般的恐怖行动。

There are other benefits of FairMOT. Research has shown that people staring at live video feeds start losing their ability to concentrate on the images after about twenty minutes. This problem has been known for some time, and the military (not to mention civilian security firms) have been seeking a technological solution. It's actually not as bad with UAVs, because the picture constantly changes, but cameras that are fixed can wear operators out real quick.
FairMOT还有其他好处。研究表明,盯着实时视频源的人们在大约二十分钟后开始失去专注于图像的能力。这个问题已经知道了一段时间了,军方(更不用说民间安防公司)一直在寻求技术解决方案。对于无人机而言,这实际上并不那么糟糕,因为画面不断变化,但是固定的摄像机会使操作员很快就疲惫不堪。

The basic tech solution is pattern analysis. Since the most common video is digital, it's possible to translate the video into numbers, and then analyze those numbers. Government security organizations have been doing this for some time but after the fact. It's one thing to have a bunch of computers analyzes satellite photos for a week, to see if there was anything useful there. It's quite another matter to do it in real-time. But computers have gotten faster, cheaper and smaller in the last few years, and programmers have kept coming up with more efficient routines for analyzing the digital images. Commercial firms already have software on the market that will analyze, in real-time, video, and alert a human operator if someone or something (you are looking for) appears to be there. The AI object detector takes advantage of faster computers and more powerful video cards to do what it does.
基本的技术解决方案是模式分析。由于最常见的视频是数字视频,因此可以将视频转换为数字,然后分析这些数字。政府安全组织已经这样做了一段时间,但事情发生之后。一堆计算机分析一个星期的卫星照片,看看那里是否有有用的东西,这是一回事。实时处理是另一回事。但是在最近几年中,计算机变得越来越快,便宜且体积越来越小,并且程序员一直在想出更有效的程序来分析数字图像。商业公司已经在市场上安装了可实时分析视频的软件,并在有人或某物(你正在寻找)出现时向操作员发出警报。人工智能(AI)对象检测器利用更快的计算机和功能更强大的视频卡来完成它的工作。

While some military analysis does not have to be real-time, like the system used in Iraq and Afghanistan to compare today's and yesterday’s photos of a road to see if a bomb may have been planted, the most common need is for real-time analysis. Several times a year now, a new software package shows up that does that or tries to. These systems are getting better. Many can definitely beat your average human observer over time (several hours of viewing video). The real-time analysis software is rapidly evolving. You don't hear much about it, because if the enemy knows the details of how it works, they can develop moves that will deceive it or, to be more accurate, make the pattern analysis less accurate. That is changing as the need for commercial AI object detector appears. For a decade this software has been used as an adjunct to human observers, and gradually taking over. There will always be a human in the loop, to confirm what the software believes it has found.
虽然某些军事分析不一定是实时的,例如伊拉克和阿富汗使用的系统可以比较今天和昨天的道路照片,以查看是否已植入炸弹,但最常见的需求是实时分析。现在一年几次,出现了一个新的软件包,它可以做到或尝试这样做。这些系统越来越好。随着时间的推移(观看视频需要几个小时),许多人肯定可以击败普通的检测人员。实时分析软件正在迅速发展。你对此了解甚少,因为如果敌人知道其运作方式的细节,他们就会发展出会欺骗它的动作,或者更准确地说,会使模式分析的准确性降低。随着对商业AI对象检测器的需求的出现,这种情况正在改变。十年来,该软件一直是人类检测人员的辅助设备,并逐渐被人们所接受。总是会有人参与,来确认软件相信它找到了什么。

But the big breakthrough, which may already have been achieved, is a predictive analytics system that can quickly examine thousands of hours of video from a specific area and calculate the probability that certain vehicles, or individuals, down there, are up to no good, or will simply be traveling down a certain road. This works if you have lots of examples of people you know, and are trying to find. The predictive analysis looks for enough indicators to make it likely that something specific is going to happen. When done in real-time, the analysis software can instantly alert users that something specific is about to happen at a specific location. If nothing does happen, that is saved and added to the library of experience the analysis software uses to make predictions. In effect, the predictive analysis software gets smarter the more often it is used. And the library of combat zone video images grows larger as well, making it possible for the analysis software to sniff more behavior patterns that predict bad actions.
但是,可能已经实现的一项重大突破是一种预测分析系统,该系统可以快速检查特定区域数千小时的视频,并计算出某些车辆或个人在该区域发生故障的概率,或只是沿着某条路行驶。如果你有很多你认识的人的例子,并且正在努力寻找的话,这个方法是有效的。预测分析寻找足够的指标,以使其预判有可能发生某些特定事件。实时完成后,分析软件可以立即提醒用户在特定位置将要发生的特定事件。如果什么也没发生,则将其保存并添加到分析软件用来进行预测的经验库中。实际上,预测分析软件使用得越频繁,它就会变得越智能。而且,作战区域视频图像库也越来越大,这使得分析软件能够嗅出更多预测不良行为的行为模式。

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