Due to shadowing, reflections, lighting conditions, and any other possible change in the environment, our background can look quite different in various frames of a video. Now obviously in the real-world this assumption can easily fail. If there is a substantial change, we can detect it - this change normally corresponds to motion on our video. Therefore, if we can model the background, we monitor it for substantial changes. The background of our video stream is largely static and unchanging over consecutive frames of a video. Well, in motion detection, we tend to make the following assumption: So why is this so important? And why do we care what pixels belong to the foreground and what pixels are part of the background? We can find this implementation in the cv2.createBackgroundSubtractorGMG function (we’ll be waiting for OpenCV 3 to fully play with this function though).Īll of these methods are concerned with segmenting the background from the foreground (and they even provide mechanisms for us to discern between actual motion and just shadowing and small lighting changes)!
![raspberry pi youtube view bot raspberry pi youtube view bot](https://hackster.imgix.net/uploads/attachments/996157/1_qFnsNi0IzdodnM0Uhe3ydQ.jpeg)
Improved adaptive Gaussian mixture model for background subtraction by Zivkovic, and Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction, also by Zivkovic, available through the cv2.BackgroundSubtractorMOG2 function.Īnd in newer versions of OpenCV we have Bayesian (probability) based foreground and background segmentation, implemented from Godbehere et al.’s 2012 paper, Visual Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive Audio Art Installation.An improved adaptive background mixture model for real-time tracking with shadow detection by KaewTraKulPong et al., available through the cv2.BackgroundSubtractorMOG function.The two primary methods are forms of Gaussian Mixture Model-based foreground and background segmentation: We use it to count the number of people walking in and out of a store.īefore we get started coding in this post, let me say that there are many, many ways to perform motion detection, tracking, and analysis in OpenCV. We use it to count the number of cars passing through a toll booth.
Raspberry pi youtube view bot how to#
In the second post in this series I’ll show you how to update the code to work with your Raspberry Pi and camera board - and how to extend your home surveillance system to capture any detected motion and upload it to your personal Dropbox.Īnd maybe at the end of all this we can catch James red handed… A little bit about background subtractionīackground subtraction is critical in many computer vision applications.
![raspberry pi youtube view bot raspberry pi youtube view bot](https://i.ytimg.com/vi/Wgk8catUoVs/maxresdefault.jpg)
This example will work with both pre-recorded videos and live streams from your webcam however, we’ll be developing this system on our laptops/desktops. The remainder of this article will detail how to build a basic motion detection and tracking system for home surveillance using computer vision techniques.
![raspberry pi youtube view bot raspberry pi youtube view bot](https://i.ytimg.com/vi/Bofo5e7oA_4/maxresdefault.jpg)
This is the first post in a two part series on building a motion detection and tracking system for home surveillance. Looking for the source code to this post? Jump Right To The Downloads Section A 2-part series on motion detection I mounted a Raspberry Pi to the top of my kitchen cabinets to automatically detect if he tried to pull that beer stealing shit again: He is my only (ex-)friend who drinks IPAs. In reality, I didn’t really see him drink the beer as my face was buried in my laptop, fingers floating above the keyboard, feverishly pounding out tutorials and articles. And after calling it quits for the night, all I wanted was to do relax and watch my all-time favorite movie, Jurassic Park, while sipping an ice cold Finestkind IPA from Smuttynose, a brewery I have become quite fond of as of late.īut that son of a bitch James had come over last night and drank my last beer. My brain was fried, practically leaking out my ears like half cooked scrambled eggs. You see, I had just spent over 12 hours writing content for the upcoming PyImageSearch Gurus course.
![raspberry pi youtube view bot raspberry pi youtube view bot](https://static.wixstatic.com/media/a27d24_c563c8e1468a46cd9b8fa71f7002f6ad~mv2.jpg)
But I muttered them to myself in an exasperated sigh of disgust as I closed the door to my refrigerator. These are words a man should never, ever have to say.