background-subtraction

Does anybody know how does it work and how to do it using OpenCV? Laplacian can be calculated using OpenCV, but the result is not what I expected. I mean I expect the image to be approximately constant contrast at background regions, but it is black, and edges are white. There are a lot of noise also, even after gauss filter. I filter image using gaussian filter and then apply laplace. I think what I want is done by a different way. Laplacian of Gaussian is an edge-detection filter; the output is 0 in constant ('background') regions, and positive or negative where there is contrast. The reason

I'm using OpenCV2.2 to implement moving objects detection with the method of Background Subtraction. And I use the Gaussian Mixture Model(GMM) method to model the background reference image. I directly get the foreground pixels(or foreground mask) by using the class cv::BackgroundSubtractorMOG provided in OpenCV2.2. It's convenient but the foreground mask returned by cv::BackgroundSubtractorMOG is not as good as I expected. In addition, it seems that cv::BackgroundSubtractorMOG performs poorer than the method of GMM wrote in C language provided in OpenCV1.0. The following is my code in OpenCV2

I'm looking to do background subtracting on an image. I'm new to MATLAB and new to image processing/analysis, so sorry if any of this sounds stupid. 1) Other than imsubtract() are there other ways to do background subtracting (besides comparing one image to another)? 2) In the Math Works explanation for imsubtract() why do they make their structuring element a disk? This seems rather difficult so far because every time I try something, I end up not only subtracting the noisy background but also losing the parts of the image I want to look at! What kind of image do you work with? Background

I am working on a robotics project using an Android phone as the main processor and the camera to detect movement. I got the Android binary package from OpenCV and got it correctly installed. I can capture images using the OpenCV native camera and display them to the screen. I'm having problems using the background subtraction class, though. I can make a new BackgroundSubtractorMOG object in the constructor, but when I attempt to run the code below, it force quits I get the error "Only 1- and 3-channel 8-bit images are supported in BackgroundSubtractorMOG" from the native code. I tried

I am trying to use cv2.createBackgroundSubtractorMOG2 () method in Python. I have tried both on my Mac and on my Raspberry Pi, and get the same error when running the following line of code: fgbg = cv2.createBackgroundSubtractorMOG2() The code I am using is taken from https://github.com/abidrahmank/OpenCV2-Python-Tutorials/blob/master/source/py_tutorials/py_video/py_bg_subtraction/py_bg_subtraction.rst I get the following error when running this code: fgbg = cv2.createBackgroundSubtractorMOG2() AttributeError: 'module' object has no attribute 'createBackgroundSubtractorMOG2' I can't seem to

I am facing some issues in implementation of the paper Statistical Background Subtraction for a Mobile Observer . Question 1 : In Section 4.1, it talks about "... the median variance is computed over the entire image from the first components ..." I am confused what the authors actually mean by this. According to Stauffer & Grimson 's paper Adaptive Background Mixture Models for Real-Time Tracking(1999), for every background model a variance gets initialized (say with value 36) and then it gets updated for each pixel. Should the median of the first model's variance across all the pixels for

I am trying to substract background from depth images acquired with kinect. When I learned what otsu thresholding is I thought that it could with it. Converting the depth image to grayscale i can hopefully apply otsu threshold to binarize the image. However I implemented (tried to implemented) this with OpenCV 2.3, it came in vain. The output image is binarized however, very unexpectedly. I did the thresholding continuously (i.e print the result to screen to analyze for each frame) and saw that for some frames threshold is found to be 160ish and sometimes it is found to be 0. I couldn't quite