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* Introduction - The solution shows panorama image from multi images. The panorama images is processing by real-time stitching algorithm... -
As you can see in the following video, I created a class that stitching n cameras in real time. https://www.youtube.com/user/feelmare/sear...
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Image size of origin is 320*240. Processing time is 30.96 second took. The result of stitching The resul... -
In past, I wrote an articel about YUV 444, 422, 411 introduction and yuv <-> rgb converting example code. refer to this page -> ht...
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refer to code: - x = 0.003 formatted_x = " {:.1e} " . format ( x ) print ( formatted_x ) # Output will be "3.0e-03"...
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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... -
The latent SVM tells the learning method used in this paper -> "Discriminatively trained deformable part models". The authors s... -
The code need to install two YouTube downloader package. Those are pytube, youtube_dl. This code try to use one of them because sometime it&...
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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...
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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