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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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Logistic Classifier The logistic classifier is similar to equation of the plane. W is weight vector, X is input vector and y is output... -
fig 1. Left: set 4 points (Left Top, Right Top, Right Bottom, Left Bottom), right:warped image to (0,0) (300,0), (300,300), (0,300) Fi... -
template < int INT> void AAA() { std::cout << "INT = " << INT << std::endl; } How to Call...
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This is example source cod of ORB_GPU feature detection and matching. ORB feature is known extraction speed is faster than surf and sift. ... -
This article explain how to access the thread index when you make block and thread with two dimensions. please refer to this page about me... -
This is dithering example, it make image like a stippling effect. I referenced to blew website. wiki page: https://en.wikipedia.org/wik... -
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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* Introduction - The solution shows panorama image from multi images. The panorama images is processing by real-time stitching algorithm... -
1. load pre-trained model 2. export onnx 3. load onnx refer to code: . import warnings from torch . jit import TracerWarning warnings . ...
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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