Pose Estimation in 7 minutes - 30 FPS on CPU Tutorial скачать в хорошем качестве

Pose Estimation in 7 minutes - 30 FPS on CPU Tutorial

Im going to show you how to implement Pose Estimation in Python and OpenCV on CPU at 30FPS all in 7 Minutes!! ⭐AI Vision Store - https://augmentedstartups.info/Vision... ⭐OpenCV Kickstarter Campaign - https://bit.ly/OpenCVKickStarter ⭐FREE AI-CV Course - https://augmentedstartups.info/yolov4... ⭐Membership + Source Code - https://bit.ly/Join_AugmentedStartups ⭐AI-CV Nano Degree - https://bit.ly/AugmentedAICVPRO This brings us to Pose estimation which is the topic of this video, we are going to explore what it is, Why and where'd you'd use it, how it works using a single camera, and how to implement 30 Frames per second Pose Estimation on CPU... All in 5 Minutes. One of the most sought after aspects of computer vision has been to understand human appearance from images and videos as well as predicting when people will like and subscribe to my channel.. haha just messing with you. Anyways Pose estimation refers to a computer vision technique that can detect human figures from a camera for body posture and gesture recognition. Pose Estimation technology enables the following applications: Assisted living, in the case of fall detection and Yoga Pose Identification, Character animation and Drone Control. Like Iv implemented in my Autonomous Drone video.. In a nutshell, It works by detecting critical body joints which can be achieved using a variety of methods, which are as follows OpenPose, AlphaPose, TFPose Estimation, and DensePose amongst many many others. Here's a link to blog post that compares the Best Human-Pose Estimation Projects : https://medium.datadriveninvestor.com... For this implementation we will be use BlazePose which is a lightweight CNN architecture for human pose estimation that is tailored for real-time inference on mobile platforms. Whats really cool is that during inference, the network produces 33 body keypoints for a single person and runs at over 30 frames per second. The Authors approach used both heatmaps and regression to aquire keypoint coodinates. This makes it particularly suited to real-time use-cases like fitness tracking and recognition. We'll be implementing Blaze pose via the MediaPipe Framework, mainly because it is fast, lightweight, accurate and suuuuper simple to implement, as you will see now in a bit. I'll have a link down below to articles that go in a bit more details of how BlazePose works on a deeper level. So the COCO topology is the standard for human body pose. It consists of 17 keypoints, which are located in the middle of the torso, arms, and legs. However, these keypoints are only used for the ankle and wrist points and do not include hands and feet which is quite limiting. With BlazePose however, they've extend the existing body keypoint set to 33 keypoints. This method allows to predict body semantics from justpose predictions alone. To implement this go to the vision store, Click on Pose Estimation, ensure that you are logged in, otherwise you can sign up. ⭐ JOIN our Membership to get access to Source Code : https://bit.ly/Join_AugmentedStartups ------------------------------------------------------------ Learn Advanced Tutorials ►https://www.Augmentedstartups.info/Te... Support us on Patreon ►https://www.AugmentedStartups.info/Pa... Chat to us on Discord ►https://www.AugmentedStartups.info/di... Interact with us on Facebook ►https://www.AugmentedStartups.info/Fa... Check my latest work on Instagram ►https://www.AugmentedStartups.info/in... ------------------------------------------------------------