Code, Computer Vision, Technology And Science

Multi-camera Capture using OpenCV (Multi-threaded)

Previously, I have managed to create a simple program to capture images from multiple cameras (click here). However, it is not quite good approach to use just a single thread to handle all of the capturing processes. Thus, I need to improve it by using multi-thread approach. Each thread will capture images from a single camera, so the number of threads will be determined by the number of cameras. In addition, to make it even better for further application (e.g.: motion detection), I made it object oriented. Continue reading

Code, Computer Vision, Technology And Science

Multi-camera Capture using OpenCV (Single-threaded)

I have been working on a project which requires me to stream multiple cameras. So, here I provide an example code to show how I can stream multiple usb cameras simultaneously using OpenCV. I assume you have installed it properly on your PC and already have basic knowledge about it. Without further ado, here is the simplest and cleanest code that I have made: Continue reading

Tips

Evernote Dark Theme Alternative

I use Evernote for PC in my daily note-taking activity. However, It hurts my eyes to look at its window due to the bright light intensity. And, I wouldn’t change my monitor setttings (brigthness/contrast) because its already perfect for me while using the other program. Since there is no official dark theme from Evernote, here is my workaround solution for those who need to use it in a “dark” theme.

Dark

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Computer Vision

HOG Person Detector Tutorial

Chris McCormick

One of the most popular and successful “person detectors” out there right now is the HOG with SVM approach. When I attended the Embedded Vision Summit in April 2013, it was the most common algorithm I heard associated with person detection.

HOG stands for Histograms of Oriented Gradients. HOG is a type of “feature descriptor”. The intent of a feature descriptor is to generalize the object in such a way that the same object (in this case a person) produces as close as possible to the same feature descriptor when viewed under different conditions. This makes the classification task easier.

The creators of this approach trained a Support Vector Machine (a type of machine learning algorithm for classification), or “SVM”, to recognize HOG descriptors of people.

The HOG person detector is fairly simple to understand (compared to SIFT object recognition, for example). One of the main reasons for this is…

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