Trainspotting With Junk, For Science

[Douglas] hometown Goshen, Indiana takes the state’s motto ‘The Crossroads of America’ seriously, at least when it comes to trains. The city is the meeting point of three heavily frequented railroad tracks that cross near the center of town, resulting in a car-traffic nightmare. When everybody agrees that a situation is bad, it is time to quantify exactly how bad it is. [Douglas] stepped up for this task and delivered.

High tech train counting equipment

He describes himself as cheap, and the gear he used to analyze the railroad traffic at a crossing visible from his home certainly fits the bill: a decades-old webcam, a scratched telephoto lens and a laptop with a damaged hinge.

With the hardware in place, the next step was to write the software to count and time passing trains. Doing this in stable conditions with reasonable equipment would pose no problem to any modern image processing library, but challenged with variable lighting and poor image quality, [Douglas] needed another solution.

Instead of looking for actual trains, [Douglas] decided to watch the crossing signals. His program crops the webcam image and then compares the average brightness of the left and right halves to detect blinking. This rudimentary solution is robust enough to handle low light conditions as well as morning glare and passing cars.

The rest is verifying the data, making it fit for processing, and then combining it with publicly available data on car traffic at the affected intersections to estimate impact. The next council meeting will find [Douglas] well prepared. Traffic issues are a great field for citizen science as shown in Stuttgart earlier. If the idea of bolting old lenses to webcams intrigues you, we got you covered as well.

Google’s Inception Sees This Turtle As A Gun; Image Recognition Camouflage

The good people at MIT’s Computer Science and Artificial Intelligence Laboratory [CSAIL] have found a way of tricking Google’s InceptionV3 image classifier into seeing a rifle where there actually is a turtle. This is achieved by presenting the classifier with what is called ‘adversary examples’.

Adversary examples are a proven concept for 2D stills. In 2014 [Goodfellow], [Shlens] and [Szegedy] added imperceptible noise to the image of a panda that from then on was classified as gibbon. This method relies on the image being undisturbed and can be overcome by zooming, blurring or rotating the image.

The applicability for real world shenanigans has been seriously limited but this changes everything. This weaponized turtle is a color 3D print that is reliably misclassified by the algorithm from any point of view. To achieve this, some knowledge about the classifier is required to generate misleading input. The image transformations, such as rotation, scaling and skewing but also color corrections and even print errors are added to the input and the result is then optimized to reliably mislead the algorithm. The whole process is documented in [CSAIL]’s paper on the method.

What this amounts to is camouflage from machine vision. Assuming that the method also works the other way around, the possibility of disguising guns (or anything else) as turtles has serious implications for automated security systems.

As this turtle targets the Inception algorithm, it should be able to fool the DIY image recognition talkbox that Hackaday’s own [Steven Dufresne] built.

Thanks to [Adam] for the tip.

Hackaday Prize Entry: Automated Wildlife Recognition

Trail and wildlife cameras are commonly available nowadays, but the Wild Eye project aims to go beyond simply taking digital snapshots of critters. [Brenda Armour] uses a Raspberry Pi to not only take photos of wildlife who wander into the camera’s field of view, but to also automatically identify and categorize the animals seen using a visual recognition API from IBM via the Node-RED infrastructure. The result is a system that captures an image when motion is detected, sends the image to the visual recognition API, and attempts to identify any wildlife based on the returned data.

The visual recognition isn’t flawless, but a recent proof of concept shows promising results with crows, a cat, and a dog having been successfully identified. Perhaps when the project is ready to move deeper into the woods, elements from these solar-powered networked birdhouses (which also use the Raspberry Pi) could help cut some cords.