The map in action, along with a sample of the video feeds.

Hardware Store Marauder’s Map Is Clarkian Magic

The “Marauder’s Map” is a magical artifact from the Harry Potter franchise. That sort of magic isn’t real, but as Arthur C. Clarke famously pointed out, it doesn’t need to be — we have technology, and we can make our own magic now. Or, rather, [Dave] on the YouTube Channel Dave’s Armoury can make it.

[Dave]’s hardware store might be in a rough neighborhood, since it has 50 cameras’ worth of CCTV coverage. In this case, the stockman’s loss is the hacker’s gain, as [Dave] has talked his way into accessing all of those various camera feeds and is using machine vision to track every single human in the store.

Of course, locating individuals in a video feed is easy — to locate them in space from that feed, one first needs an accurate map. To do that, [Dave] first 3D scans the entire store with a rover. The scan is in full 3D, and it’s no small amount of data. On the rover, a Jetson AGX is required to handle it; on the bench, a beefy HP Z8 Fury workstation crunches the point cloud into a map. Luckily it came with 500 GB of RAM, since just opening the mesh file generated from that point cloud needs 126 GB. That is processed into a simple 2D floor plan. While the workflow is impressive, we can’t help but wonder if there was an easier way. (Maybe a tape measure?)

Once an accurate map has been generated, it turns out NVIDIA already has a turnkey solution for mapping video feeds to a 2D spatial map. When processing so much data — remember, there are 50 camera feeds in the store — it’s not ideal to be passing the image data from RAM to GPU and back again, but luckily NVIDIA’s “Deep Stream” pipeline will do object detection and tracking (including between different video streams) all on the GPU. There’s also pose estimation right in there for more accurate tracking of where a person is standing than just “inside this red box”. With 50 cameras, it’s all a bit much for one card, but luckily [Dave]’s workstation has two GPUs.

Once the coordinates are spat out of the neural networks, it’s relatively simple to put footprints on the map in true Harry Potter fashion. It really is magic, in the Clarkian sense, what you can do if you throw enough computing power at it.

Unfortunately for show-accuracy (or fortunately, if you prefer to avoid gross privacy violations), it doesn’t track every individual by name, but it does demonstrate the possibility with [Dave] and his robot. If you want a map of something… else… maybe check out this backyard project.

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The TAK Ecosystem: Military Coordination Goes Open Source

In recent years you’ve probably seen a couple of photos of tablets and smartphones strapped to the armor of soldiers, especially US Special Forces. The primary app loaded on most of those devices is ATAK or Android Tactical Assault Kit. It allows the soldier to view and share geospatial information, like friendly and enemy positions, danger areas, casualties, etc. As a way of working with geospatial information, its civilian applications became apparent, such as firefighting and law-enforcement, so CivTAK/ATAK-Civ was created and open sourced in 2020. Since ATAK-Civ was intended for those not carrying military-issued weapons, the acronym magically become the Android Team Awareness Kit. This caught the attention of the open source community, so today we’ll dive into the growing TAK ecosystem, its quirks, and potential use cases.

Tracking firefighting aircraft in 3D space using ADS-B (Credit: The TAK Syndicate)

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Hackaday Prize Entry: A 3D Mapping Drone

Quadcopters show a world of promise, and not just in the mediums of advertising and flying Phantoms over very large crowds. They can also be used for useful things, and [Sagar]’s entry for The Hackaday Prize does just that. He’s developing a 3D mapping drone for farmers, miners, students, and anyone else who would like high-resolution 3D maps of their local terrain.

Most high-end mapping and photography work done with quadcopters these days uses heavy DSLRs to record the images that are brought back to the base station to be stitched into a 3D image. While this works, those GoPros are getting really, really good these days, and with 4k resolution, too. [Sagar] is mounting one of these to a custom quad and flying around an area to get images of an area from every angle.

To stitch the images together [Sagar] will be using the Pix4D mapping software, an impressive bit of software that will convert a multitude of still images to a 3D scene. It’s an expensive piece of software – $8500 for a perpetual license, but the software can be rented for $350/month until a FOSS alternative can be developed.


The 2015 Hackaday Prize is sponsored by:

3D Mapping Of Rooms, Again

Last year we saw what may be the coolest application of a Kinect ever. It was called Kintinuous, and it’s back again, this time as Kintinuous 2.0, with new and improved features.

When we first learned of Kintinuous, we were blown away. The ability for a computer with a Kinect to map large-scale areas has applications as diverse as Google Street View, creating custom Counter-Strike maps, to archeological excavations. There was one problem with the Kintinuous 1.0, though: scanning a loop would create a disjointed map, where the beginning and end of a loop would be in a different place.

In the video for Kintinuous 2.0, you can see a huge scan over 300 meters in length with two loops automatically stitched back into a continuous scan. An amazing feat, especially considering the computer is processing seven million vertices in just a few seconds.

Unfortunately, it doesn’t look like there will be an official distribution of Kintinuous 2.0 anytime soon. The paper for this Kintinuous is still under review, and there are ‘issues’ surrounding the software that don’t allow an answer to the if and when question of release. Once the paper is out, though, anyone is free to reimplement it, and we’ll gladly leave that as an open challenge to our readers.

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