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Google Coral USB Edge TPU Implementation Guide 1. Installation and Troubleshooting 1.1 Hardware Requirements Google Coral USB Accelerator ...
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ONNX Runtime with ROCm (AMD GPU) Setup Guide Installation Prerequisites ROCm installed (6.0+ recommended) Python 3.8-3.10 Install ONNX Runti...
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In past, I wrote an articel about YUV 444, 422, 411 introduction and yuv rgb converting example code. refer to this page -> http://feel...
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Video stabilization example source code. The principle is like that... Firstly, to obtain 2 adjacent images extract good feature to t... -
This is GPU version of this post. http://feelmare.blogspot.kr/2014/04/opencv-study-calcopticalflowfarneback.html In the GPU mode, the ret...
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Image size of origin is 320*240. Processing time is 30.96 second took. The result of stitching The resul... -
This is example code for drawing fan shape grid using vtkStructuredGrid in vtk. I've looked at a lot of examples that draw like thi... -
Complete Tutorial: LabelMe with Login System and Dataset Management This guide provides step-by-step instructions for setting up LabelMe wit...
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Optical Flow sample source code using OpenCV. It programed based on http://feelmare.blogspot.kr/2012/10/make-2-frame-having-time-interv... -
In the YUV color format, Y is bright information, U is blue color area, V is red color area. Show the below picture. The picture is u-v col...
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ReplyDeleteThis one is better than "More stringent pedestrian detection by Deep learning verification" and "More stringent pedestrian detection by HOG verification". Do you use OpenCV to achieve such result?
ReplyDeleteDetecting pedestrian using Deep learning is recorded high rate false positive, but not sensitive variable pedestrian pose. and processing speed is not good.
DeleteHog is fast, but to detect typical appearance when training data pose.
So, I can not say yet, which way is better..
Thank you.
can you share this source code! Thank you
ReplyDelete