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As I previously mentioned, I recently ditched my Wyze cams and moved over to Eufy cams for indoor.
I also decided to buy some outdoor cams to mount and watch my front door and driveway in real time.
So I started to explore ways of weeding out the false positive alerts and only getting alerts when there actually was a person at my door or a car in my driveway.
Went through a few set ups but I've found one that has been working terrifically for a few days so wanted to share. I wanted to do POE but due to house design issues I couldn't run the necessary wiring.
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.
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.
While DeepStack restricts the number of actions in its lookahead trees, it has no need for explicit abstraction as each re-solve starts from the actual public state, meaning DeepStack always perfectly understands the current situation.
We evaluated DeepStack by playing it against a pool of professional poker players recruited by the International Federation of Poker. Eleven players completed the requested 3, games with DeepStack beating all but one by a statistically-significant margin.
Over all games played, DeepStack outperformed players by over four standard deviations from zero. Until DeepStack, no theoretically sound application of heuristic search was known in imperfect information games.
Sparse lookahead Trees. About the Algorithm The first computer program to outplay human professionals at heads-up no-limit Hold'em poker 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.
A fundamentally different approach 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.
LBR DeepStack vs.Face Match. This lets DeepStack avoid computing a complete strategy in advance, skirting the need for explicit abstraction. A fundamentally different approach DeepStack is the first theoretically sound application of heuristic search methods—which have been famously successful in games Snooker Championship 2021 checkers, chess, and Go—to imperfect information 1001 Spiele Barbie. At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. DeepStack vs.