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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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* Introduction - The solution shows panorama image from multi images. The panorama images is processing by real-time stitching algorithm... -
Image size of origin is 320*240. Processing time is 30.96 second took. The result of stitching The resul... -
This is data acquisition source code of LMS511(SICK co.) Source code is made by MFC(vs 2008). The sensor is communicated by TCP/IP. ... -
Creating a custom CUDA kernel that directly utilizes tensor cores is an advanced topic, as tensor cores are typically accessed through highe...
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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... -
Created Date : 2011.2 Language : C/C++ Tool : Microsoft Visual C++ 2010 Library & Utilized : OpenCV 2.2 Reference : Interent Refer... -
Created Date : 2009.10. Language : C++ Tool : Visual Studio C++ 2008 Library & Utilized : Point Grey-FlyCapture, Triclops, OpenCV... -
Background subtractor example souce code. OpenCV support about 3 types subtraction algorithm. Those are MOG, MOG2, GMG algorithms. Det... -
The MNIST dataset is a dataset of handwritten digits, comprising 60 000 training examples and 10 000 test examples. The dataset can be downl...
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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