This is a step-by-step tutorial on how to use tensorflow.js to run an object detection web application for your webcam feed in real-time. The model used is COCO-SSD-mobilenet_v2.
Readit Newsmanneshiva commented on In-Browser Deep Learning: Object Detection Using Tensorflow.js nanonets.com/blog/object-... · Posted by u/manneshiva
manneshiva commented on Human Pose Estimation with Deep Learning blog.nanonets.com/human-p... · Posted by u/ole_gooner
They showed an app like this running on a phone in laggy but real time at I/O yesterday
Interesting, wonder what their use case was?
manneshiva commented on Human Pose Estimation with Deep Learning blog.nanonets.com/human-p... · Posted by u/ole_gooner
How fast are the algorithms? does anyone of them work in real time?
A news agency in China uses an AI news anchor.
The speed at which China seems to be adopting and implementing the latest deep learning technology into everyday life is truly astounding. There is no doubt it is going to be one of the top countries contending for the world leader in AI in the next decade or so.
manneshiva commented on Motion Estimation with Optical Flow blog.nanonets.com/optical... · Posted by u/ole_gooner
Back in 2008 there was an amazing demo of realtime dense optical flow on a GPU[0] but all the links are dead now. I've searched hard for another comparable implementation since then but had no luck.
Does anyone have a hint on what technique they might have used?
I have seen similar results using Gunner Farneback's algorithm.
Highlights of this technique:
Simple: One-sentence method summary: use keypoint detection technic to detect the bounding box center point and regress to all other object properties like bounding box size, 3d information, and pose.
Versatile: The same framework works for object detection, 3d bounding box estimation, and multi-person pose estimation with minor modification.
Fast: The whole process in a single network feedforward. No NMS post processing is needed. Our DLA-34 model runs at 52 FPS with 37.4 COCO AP.
Strong: Our best single model achieves 45.1AP on COCO test-dev.
Easy to use: We provide user friendly testing API and webcam demos.