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Deepstack Approach #1 VideoDeepStack AI plays Mike McDonald DeepStack is an AI server that empowers every developer in the world to easily build state-of-the-art AI systems both on premise and in the cloud. The promises of Artificial Intelligence are huge but becoming a machine learning engineer is hard. DeepStack is device and language agnostic. You can run it on Windows, Mac OS, Linux, Raspberry PI and use it with any programming language. 77 rows · DeepStack Extravaganza Poker Tournament October 26–November 29, More Than . 8/27/ · What I had set up based on the above approach was 1) BlueIris would detect motion and take a snapshot, 2) the AITools application would send the snapshot over to Deepstack, 3) if Deepstack indicated it was a person/car, AITools would then trigger the camera, which 4) would send an MQTT alert to HomeAssistant to take a snapshot of the stream.
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So far the W's are working great. AI Software - There's a LOT of options out there, some of which are local and some of which rely on various cloud setups.
Personally I wanted to only use local processing. I personally settled on using Deepstack which I have found to be fairly accurate and when configured correctly not overkill on my server.
Though I'm sure if I figured out a way of adding some kind of Coral-like device the processing would get significantly faster.
In a nutshell, his set up using BlueIris to take a snapshot when motion is detected on the lower definition substream of his camera, which is then processed by Deepstack to determine if there's a person.
If there is, it triggers recording in full 4k resolution. I followed this approach at first and was very impressed at how simple it was.
And it worked. However, I quickly ran into a limitation for my own use case. I wasn't concerned with my cameras all recording at full quality the Eufy ones don't have a substream but I wanted a snapshot sent to my phone when the camera detected a person or a car at the front door or in the driveway.
The result was, as you might expect, that often times the person would no longer be in the frame by the time HomeAssistant took a snapshot due to the lag of DeepStack processing even though it was only a few seconds.
So that forced me back to the drawing board. The only other way to get images out of AITools was via Telegram. But the downside here was that there was no way to filter out snapshots when I didn't want them ie when the front door is opened by me to go get the mail.
So no more middleman and trying to get the images synced up. To do this, I found this custom component.
There's also one that can detect faces but I have not given that a go. The instructions on the GitHub page for installing Deepstack via Docker and getting everything set up are pretty well done.
The noavx image works fine so that's what I went with. DeepStack avoids reasoning about the full remaining game by substituting computation beyond a certain depth with a fast-approximate estimate.
DeepStack considers a reduced number of actions, allowing it to play at conventional human speeds. The system re-solves games in under five seconds using a simple gaming laptop with an Nvidia GPU.
In a study completed December and involving 44, hands of poker, DeepStack defeated 11 professional poker players with only one outside the margin of statistical significance.
AI research has a long history of using parlour games to study these models, but attention has been focused primarily on perfect information games, like checkers, chess or go.
Poker is the quintessential game of imperfect information, where you and your opponent hold information that each other doesn't have your cards.
Until now, competitive AI approaches in imperfect information games have typically reasoned about the entire game, producing a complete strategy prior to play.
DeepStack is the first theoretically sound application of heuristic search methods—which have been famously successful in games like checkers, chess, and Go—to imperfect information games.
At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play.
This lets DeepStack avoid computing a complete strategy in advance, skirting the need for explicit abstraction.
We train it with deep learning using examples generated from random poker situations. DeepStack is theoretically sound, produces strategies substantially more difficult to exploit than abstraction-based techniques and defeats professional poker players at heads-up no-limit poker with statistical significance.
DeepStack Implementation for Leduc Hold'em. DeepStack vs. IFP Pros. Twitch Streamers Season 1. The performance of DeepStack and its opponents was evaluated using AIVAT , a provably unbiased low-variance technique based on carefully constructed control variates.
Despite using ideas from abstraction, DeepStack is fundamentally different from abstraction-based approaches, which compute and store a strategy prior to play.