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Showing posts from February, 2019

Deep learning by Yolo Darknet vs HAAR and LBP cascades in people detection

I just performed people detection by Yolo model in Opencv 4 DNN module. I analyzed my old dataset, which was used for the same task performed by Haar, LBP as well.  I will let the evaluation on your visual feeling. Let me know if there is an application out there building on HAAR, LBP. I think that even the cameras head focus/tracking systems are no more based on HAARs. Your comments are welcomed on facebook.  YOLO DARKNET (CPU performance) people detection in opencv 4  300 ms per image- width=316 --height=510 --scale=0.00392 LBP based detectMultiscale   HAAR based detectMultiscale   HAAR based detectMultiscale

Opencv 4 Linux application in visual studio 2017- Ubuntu Linux subsystem on windows 10

This tutorial is step by step guide for the development of Opencv 4 Linux application in Visual Studio 2017 under windows. The motivation is simple. I would like to work in Visual Studio 2017, which is simply great. I personally love it. It is not the only motivation. I am a windows user, but the market is full of Linux based cloud machines and docker. So, I need to target Linux. The OpenCV running on Linux is faster from my 5-year experience(consider any possible optimization options). Another motivating point is a windows file system. Write an application for Linux that accesses the windows file system. Again, I am windows person with lots of Linux experience, lots of datasets, images, and video stored on windows machines. I would like to use this data without any transfer or copy of my datasets. Just directly open c:/myPositiveImages image directory in Linux app as /mnt/c/myPositiveImages. Yes, it is possible.  I am doing this to prepare my datasets stored in the