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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... -
Google Coral USB Edge TPU Implementation Guide 1. Installation and Troubleshooting 1.1 Hardware Requirements Google Coral USB Accelerator ...
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Image size of origin is 320*240. Processing time is 30.96 second took. The result of stitching The resul... -
I use MOG2 algorithm to background subtraction. The process is resize to small for more fast processing to blur for avoid noise affectio...
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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... -
HIP (Heterogeneous - Compute Interface for Portability) provides similar functionality to CUDA streams for asynchronous execution . The...
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You have already installed Visual studio 2012 or other version. And, you have to install CUDA toolkit, go to the nvidia homepage -> htt...
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Created Date : 2011.2 Language : C/C++ Tool : Microsoft Visual C++ 2010 Library & Utilized : OpenCV 2.2 Reference : Interent Refer...
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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