background-subtraction

问题 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

问题 I want to test ViBe algorithm for Background Subtraction. Currently I am using opencv libraries. I found out a sample implementation in opencv/samples/gpu/bgfg_segm.cpp and bgfg_vibe.cpp files. These files are under gpu module. Now I have a system without GPU. And when I try to run the code, it crashes on the initialization of the first frame. Can anybody tell me how to solve this issue? Thanks in advance. 回答1: pseudo codes suck big time! here's the un-pseudo/ed version. results?:there is

问题 A guy posted this tutorial about object tracking using Kalman filter. Many people rated high star so it is not a fault/wrong tutorial. However, a guys posted the following question:" In this code you have done detection in every frame and this output is provided as the input to the kalman filter.So background subtraction and kalman filter will give similar results.So please can you explain the use of kalman filter here. " I have the same thought with him. Can anybody explain the use of Kalman

问题 I am rather new to OpenCV and image processing in general. I am looking into background subtraction to facilitate motion tracking (people counting). Looking at the openCV documentation on background subtracting, GMG gives rather nice results. Also when looking at a video comparing the methods, I feel that GMG gives the best results, at least for my purpose. I installed the latest version of opencv to use with python3 thus: git clone --depth=1 https://github.com/Itseez/opencv.git cd opencv

问题 I've used below tutorial to do background subtraction, http://docs.opencv.org/master/d1/dc5/tutorial_background_subtraction.html#gsc.tab=0 But using pMOG2->apply( frame, fgMaskMOG2 ) method return output as a binary image. Is there any method to get only color objects after removing the background or get color image using binary image? 回答1: One thing you can do is to use the binary image as a mask for coping the objects from the color image into another image: // create an image like frame

问题 I am using visual 2010 (C++) with opencv 2.3.1 to construct this code for background subtraction using MOG2. As shown in the full code here it works successfully but with much noise. can anyone suggest on how to reduce this noise. some one (thanks for him) has told me to increase the size of the kernel instead of using this morphological function: void morphOps(Mat &thresh){ Mat erodeElement = getStructuringElement( MORPH_RECT,Size(2,2)); //3x3 Mat dilateElement = getStructuringElement( MORPH

问题 I am doing some work regarding tracking a person, I am using this dataset. I am trying right now to extract foreground using background subtraction method i.e. Mean Filter My background is like and if I try to subtract my current frame like this so after subtraction I am getting image like this and after thresholding of 0.15 or 38 I get this mask So if you notice this mask, it is splitting this foreground in to two pieces because of occlusion of person and chair. I dont know how to solve this

问题 I am trying to substract this building brick. . For that I am using the KNN algorithm provided by opencv 3.0. To initialize the background model I am using 40 frames without the brick. All in all it works pretty well. (Brick with Shadow) The only problem is that the algorithm starts loosing the brick around Frame 58 (Image shows frame 62) After frame 64 I get only black images. I know this wouldn't happen if the brick would move, but unfortunatly there are long sequences where it doesn`t.

问题 I am trying to detect foreground motion using opencv2 by removing static (mostly) BG elements. The method I am using is based on taking the mean of a series of images - representing the background. Then calculating one Standard deviation above and below that mean. Using that as a window to detect foreground motion. This mechanism reportedly works well for moderately noisy environments like waving trees in the BG. The desired output is a mask that can be used in a subsequent operation so as to

问题 I am working on a project which requires detection of people in a scene. Initially after running the HOG detector on the original frames a particular background object was being detected as a person on all the frames, giving me 3021 false positive detections. So I took the logical step to remove the static background by applying a background subtracter (BackgroundSubtractorMOG2) to all the frames. The resulting frames looked like this: Then these mask images were added (using bitwise_and) to