Actually Locating Stealth Fighters with Cheap Cameras Without using AI or Radar in Real time.

Summary

The video by Consistently Inconsistent explores a novel technique to locate stealth fighters using simple cameras, bypassing AI and radar. Through pixel motion voxel projection, stealth fighters like the F-35 can be detected from a significant distance using a few low-resolution cameras. This method involves projecting rays from pixels to a voxel grid, enabling detection of fast-moving objects by analyzing intersecting movement lines. It's not only efficient but also cost-effective, with potential applications in detecting drones, asteroids, and even improving air traffic safety by tracking birds. With an estimated cost of a few hundred million, this technique promises to transform surveillance and detection across various fields.

Highlights

• Spot stealth fighters with just regular cameras using pixel motion voxel projection! 🤯
• F-35 detected as a tiny distant smudge thanks to motion analysis. 😲
• Projection technique also helps in spotting drones from kilometers away! 🚀
• This method could revolutionize how we track and predict asteroids. 🌌
• Efficient and powerful—runs quickly even on basic hardware! ⚡

Key Takeaways

• Detecting stealth fighters is possible with cheap, low-res cameras! 🎥
• Pixel motion voxel projection: the secret sauce to spotting the stealthy F-35. ✈️
• Cost-effective and can revolutionize surveillance and detection techniques. 💸
• Works wonders for drone detection and improves tracking near airports. 🚁
• Unleashing potential for asteroid detection and beyond! ☄️

Overview

In this innovative video, creator Consistently Inconsistent reveals how stealth fighters, such as the F-35, can be detected using low-resolution cameras, bypassing the need for AI or radar. The key lies in a technique called pixel motion voxel projection. By analyzing movement through intersecting lines from multiple camera angles, the method provides an effective way of spotting fast-moving objects, even those camouflaged like stealth jets.

This technique isn't limited to fighter jets. It can also detect drones from several kilometers away, track birds near airports to enhance air travel safety, and even improve our ability to locate potentially devastating asteroids. The method is efficient, requiring only a modest investment, and leverages the statistical impossibility of many movement rays intersecting without object presence in the voxel to pinpoint targets.

The video illustrates the broad applicability of this approach, highlighting its potential to reshape surveillance and detection. By democratizing access to such technology at a fraction of traditional costs, it opens new avenues for enhancing global safety and monitoring skies extensively, even with existing equipment. From telescopes to simple cameras, the possibilities are vast and transformative.

Chapters

• 00:00 - 00:30: Introduction to Pixel Motion Voxel Projection The chapter introduces the concept of Pixel Motion Voxel Projection and its practical applications. It describes a scenario where this technique allows for the identification of a stealth F-35 fighter jet in an unzoomed cell phone photo taken 30 km away. The method does not require AI and can work with low-resolution videos from inexpensive wide-angle cameras, highlighting its simplicity and effectiveness in detecting objects like drones.
• 00:30 - 03:00: Drone Detection and Asteroid Projection This chapter focuses on innovative technologies in drone detection and asteroid projection. It explains how advanced pixel projection can revolutionize the detection and tracking of asteroids capable of causing wide-scale destruction. The process begins by selecting specific pixels for projection into a voxel grid. Because aircraft like F35 jets may appear as only a few pixels at a great distance, this method utilizes their predictable movement patterns and color subtraction techniques to accurately locate and track them within images. This approach represents a significant advancement in predictive and detection capabilities, ensuring better preparation for potential threats.
• 03:00 - 05:00: Efficient Processing and Potential Applications The chapter discusses the evolution of capturing motion in images, focusing on the ability to notice subtle changes in each pixel's color value due to the movement of objects. The issue highlighted is the difficulty in distinguishing motion caused by objects at different distances and speeds (like a close, slow-moving bird vs. a distant, fast-moving F35) when using traditional 2D images. It suggests a potential solution by projecting a ray from each pixel back from the camera, implying a more advanced method of processing or imaging.
• 05:00 - 07:30: Bird and Asteroid Detection Benefits The chapter explores the methodology and benefits of detecting birds and asteroids using a system of cameras. These cameras capture rays from various angles, transforming them into a grid of voxels where the motion from each ray's pixel is added to the respective voxels it hits. As more cameras oriented differently are added to the system, it becomes statistically improbable for numerous high-movement rays to intersect at a single voxel without the presence of a moving object, like a bird or asteroid. Despite potential noise and interference from objects like birds and flies near the camera, these appear as thin lines that average out without significantly affecting the detection accuracy.
• 07:30 - 09:00: Upcoming Projects and Conclusion The chapter discusses upcoming projects and some concluding thoughts. It focuses on the technical details of a video processing technique that efficiently uses pixel to voxel projection. This process is likened to pandas walking on railway tracks, emphasizing its parallel nature, akin to a Minecraft style of ray tracing. The current implementation runs in a second using a single CPU thread, but shifting the process to a graphics card could enable real-time performance, as a more efficient use of graphical resources compared to activities like NFT minting.

Actually Locating Stealth Fighters with Cheap Cameras Without using AI or Radar in Real time. Transcription

• 00:00 - 00:30 can you spot the stealth F-35 fighter jet as it would appear on your unzoomed cell phone while flying 30 km away in this photo well by using this pixel motion voxal projection technique I can tell you with absolute confidence that is this tiny little smudge right here without even needing to use AI and all the information I needed to be able to find it with a few lowresolution videos just like these from a few cheap wide-angle cameras not only that but this technique can also be used to easily detect any drones from a few
• 00:30 - 01:00 kilometers away which you can see here with the little lines that intersect each other and entirely overhaul the way that we can find and project asteroids that can level entire cities without warning so the first step in pixelox projection is to determine what pixels we want to select for when we project them into the voxal grid and since that this distance F35s will only be a few pixels across instead of trying to recognize them we can use our knowledge that fighter jets tend to you know move to help determine where in the image the fighter jet is by subtracting the colors
• 01:00 - 01:30 from where it was before from where it is now in a sequence of images leaving us with an image that only shows how much each pixel's color value changes from the movement of objects between each frame allowing us to notice all sorts of previously hard to see details the problem with this is that on a 2D image it is pretty much impossible to tell whether this motion is from a slowmoving close by bird or a far away fastmoving F35 but if we take these images and project a ray from each of the pixels back out from the camera in
• 01:30 - 02:00 the direction that they were taken from into a grid of voxels and add the motion of that rays pixel to each voxal it hits and then repeat this from a bunch of different cameras we get this because as you add more and more randomly oriented cameras it very quickly becomes statistically impossible that there will be this many high movement rays intersecting into this one spot without there actually being something moving in that voxil to cause it so even if the image is extremely noisy and there are objects such as birds and flies in front of the camera they will appear as these thin lines that don't hit anything else and very quickly average out especially
• 02:00 - 02:30 since you can repeat this for each frame in the video as they move apart not only that but it can also run extremely efficiently this pixel to voxal projection is much like a bunch of pandas walking at top railway tracks and that it's an embarrassingly parallel problem as it's basically Minecraftified ray tracing and my code already only takes a second to run on my PC off of a single CPU thread so once you use a graphics card for this instead of using it to mint the just one more NFT needed to establish true crypto libertarianism it should run in real time even off of a
• 02:30 - 03:00 potato pretty much the only slightly hard part about using this is finding out the camera's orientation since small errors in orientation will add up over large distances but even this is trivial to fix and thanks to don't ask don't tell you don't even have to worry about the camera's sexual orientation i honestly estimate it would only cost at most a few hundred million to be able to detect any F35 over the entire US from over a 100 km away so there will be at least a few billion dollars left over
• 03:00 - 03:30 for any government contractor to steal his profit to bribe politicians for more contracts to steal from afterwards and it really wouldn't be that hard to hide these cameras in other countries and on Seabo to know where planes are everywhere in the world and despite their smaller size this technique works even better for locating drones since over shorter distances there will be much less atmospheric distortion meaning you could use this to cheaply and passively detect any drone within a few kilometers of a camera array which in particular means it is incredible at
• 03:30 - 04:00 detecting any birds that are anywhere near flight path at airports which is normally basically impossible to do accurately but with a few cameras it is now easy to track the precise location of even thousands of small birds down to a few centimeters before they end up in the Hudson River not only that but this is perfect at massively improving our ability to detect asteroids especially those which are big enough to level entire cities but not large enough to reflect enough light to be detected which becomes trivial once you apply
• 04:00 - 04:30 this technique using telescopes from around the world and especially in space for example try and spot the asteroid in this photo impossible right well normally the way this is done is using our old friend motion extraction to find bright spots as they move across the sky from a single telescope but if you use voxal projection you can use a parallax which normally prevents you from combining the light from multiple distant telescopes from around the world to your advantage as the small amount of light that the asteroid adds to the noise in the image which would normally get entirely lost as noise will instead
• 04:30 - 05:00 get added to the voxal grid and exponentially accumulate statistically in ways that don't even happen with long exposures from single telescopes and that's before you stack these in a sequence i cannot begin to overstate just how much easier it is to detect them with this it would make detecting Planet 9 trivial even using existing telescopes which I was originally planning on doing but as much as I want to name it after my cat it also would take a lot of time to go through the data sets and after recent tragic events I realized how good it would be at
• 05:00 - 05:30 detecting birds so I figured I would post this to get the word out before they cause another plane crash i am planning on making a video where I use this to detect space junk and find asteroids in the near future but for now all of the code for this will be in the description and feel free to share this video with your friends and if anyone watching this has the ability to organize multiple telescopes from across the globe to image an overlapping area in the sky especially if it intersects a view of the James Web Space Telescope or Uklid at that moment can you please send me some of the Fitz files my Twitter is
• 05:30 - 06:00 in the description below anyways thanks for watching hope you enjoyed share it if you want and I'll see you in the next one