In short, there are too many different kinds of approaches to AI in surveillance cameras to have a universally effective camouflage. In many cases there is sim

In short, there are too many different kinds of approaches to AI in surveillance cameras to have a universally effective camouflage. In many cases there is simply not enough pixels on target for these patterns to render in a way that makes them at all distinguishable. You also have to account for the fact that you may wind up viewing a person from any range of a 360 arc/angle, so the pattern would need to be around the entire jacket. Most algorithms are looking for more of an overall target size and proportions, plus things like target location relative to an artificial horizon or ground plane. In some cases, this might work to reduce the overall classification confidence, but is unlikely to truly make the person "invisible". Also, thermal cameras are hardly used anymore. They have been stuck at relatively low resolutions (D1 / 640x480), and modern sensors have really good low-light imaging. Because thermal cameras are still very costly, and because they never produce a "good" image with any identifiable detail, they have become really really rarely used overall. Even so, a few patches on a jacket that show small regions of high thermal contrast are unlikely to fool any systems. I doubt that these researchers had access to current state of the art perimeter protection analytics products. The most likely tested on lower end easily available consumer based products. It is hard to say what would be effective overall that is practical. Many systems ultimately fail on people crawling, some will detect this, but often at the trade off of many false alarms, so it is usually not enabled. However, crawling around is not really that practical. Large groups of people moving very closely together are harder to detect, particularly if they are all dressed very similarly. But, I wouldn't call this a reliable evasion technique.