Project Shows How To Use Machine Learning to Detect Pedestrians

Most people are familiar with the idea that machine learning can be used to detect things like objects or people, but for anyone who’s not clear on how that process actually works should check out [Kurokesu]’s example project for detecting pedestrians. It goes into detail on exactly what software is used, how it is configured, and how to train with a dataset.

The application uses a USB camera and the back end work is done with Darknet, which is an open source framework for neural networks. Running on that framework is the YOLO (You Only Look Once) real-time object detection system. To get useful results, the system must be trained on large amounts of sample data. [Kurokesu] explains that while pre-trained networks can be used, it is still necessary to fine-tune the system by adding a dataset which more closely models the intended application. Training is itself a bit of a balancing act. A system that has been overly trained on a model dataset (or trained on too small of a dataset) will suffer from overfitting, a condition in which the system ends up being too picky and unable to usefully generalize. In terms of pedestrian detection, this results in false negatives — pedestrians that don’t get flagged because the system has too strict of an idea about what a pedestrian should look like.

[Kurokesu]’s walkthrough on pedestrian detection is great, but for those interested in taking a step further back and rolling their own projects, this fork of Darknet contains YOLO for Linux and Windows and includes practical notes and guides on installing, using, and training from a more general perspective. Interested in learning more about machine learning basics? Don’t forget Google has a free online crash course to get you up to speed.

All the Unofficial Electronic Badges of DEF CON

2015 was the year of the unofficial hardware badge at DEF CON 23. There were a ton of different hardware badges designed for the love of custom electronics and I tried to catch up with the designer of each different badge. Here is the collection of images, video demos, and build details for each one I saw this weekend.

Whiskey Pirates

[TrueControl] did a great job with his badge design this year for the Whiskey Pirate Crew. This is a great update from the badge he designed last year, keeping the skull and bones outline. It uses a PSOC4 chip to control a ton of LEDs. The eyes are RGB pixels which are each on their own PCB that is soldered onto the back of the badge, with openings for the LED to show through. Two AA batteries power the board which has a surface-mount LED matrix. The user controls are all capacitive touch. There is a spinner around one eye, and pads for select and back. The NRF24L01 radio operates at 2.4GHz. This badge is slave to commands from last year’s badge. When the two are in the same area the 2015 badges will scroll the nickname of the 2014 badge it “sees”. The piezo element also chirps many different sounds based on the interactions with different badges.

[True] makes design an art form. The matte black solder mask looks fantastic, and he took great care in use of font, size, alignment, and things like letting copper show through for a really stunning piece of hardware art.

Keep reading for ten more great badges seen over the weekend.

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Ask Hackaday: A Robot’s Black Market Shopping Spree

It was bad when kids first started running up cell phone bills with excessive text messaging. Now we’re living in an age where our robots can go off and binge shop on the Silk Road with our hard earned bitcoins. What’s this world coming to? (_sarcasm;)

For their project ‘Random Darknet Shopper’, Swiss artists [Carmen Weisskopf] and [Domagoj Smoljo] developed a computer program that was given 100 dollars in bitcoins and granted permission to lurk on the dark inter-ether and make purchases at its own digression. Once a week, the AI would carrying out a transaction and have the spoils sent back home to its parents in Switzerland. As the random items trickled in, they were photographed and put on display as part of their exhibition, ‘The Darknet. From Memes to Onionland’ at Kunst Halle St. Gallen. The trove of random purchases they received aren’t all illegal, but they will all most definitely get you thinking… which is the point of course. They include everything from a benign Lord of the Rings audio book collection to a knock-off Hungarian passport, as well as the things you’d expect from the black market, like baggies of ecstasy and a stolen Visa credit card. The project is meant to question current sanctions on trade and investigate the world’s reaction to those limitations. In spite of dabbling in a world of questionable ethics and hazy legitimacy, the artists note that of all the purchases made, not a single one of them turned out to be a scam.

Though [Weisskopf] and [Smoljo] aren’t worried about being persecuted for illegal activity, as Swiss law protects their right to freely express ideas publicly through art, the implications behind their exhibition did raise some questions along those lines. If your robot goes out and buys a bounty of crack on its own accord and then gives it to its owner, who is liable for having purchased the crack?

If a collection of code (we’ll loosely use the term AI here) is autonomous, acting independent of its creator’s control, should the creator still be held accountable for their creation’s intent? If the answer is ‘no’ and the AI is responsible for the repercussions, then we’re entering a time when its necessary to address AI as separate liable entities. However, if you can blame something on an AI, this suggests that it in some way has rights…

Before I get ahead of myself though, this whole notion circulates around the idea of intent. Can we assign an artificial form of life with the capacity to have intent?