background-subtraction

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 minimise further processing. Specifically I am going to use optical flow detection within that region.

I am working on a project to detect object of interest using background subtraction and track them using optical flow in OpenCV C++. I was able to detect the object of interest using background subtraction. I was able to implement OpenCV Lucas Kanade optical flow on separate program. But, I am stuck at how to these two program in a single program. frame1 holds the actual frame from the video, contours2 are the selected contours from the foreground object. To summarize, how do I feed the forground object obtained from Background subtraction method to the calcOpticalFlowPyrLK ? Or, help me if my

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 the original image so the white pixels are replaced the pixels constituting the person. Sample: Then I

For what kind of applications can i use dsp core of beagleboard? Can i use the DSP acceleration for background subtraction algorithm in OpenCV? You can use the DSP for all kinds of computations. It is a general purpose CPU optimized for DSP applications. So yes, even floating point stuff will work albeit the performance will not be great. The DSP really shines if you do integer computations over large arrays of data. Here the DSP can easily compute so fast that the time to transfer data from and to memory becomes the bottleneck. To give you a figure what is possible: I have an algorithm

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 filter here? A simple detection with background subtraction will give a result in every sample period,

Using images of articles of clothing taken against a consistent background, I would like to make all pixels in the image transparent except for the clothing. What is the best way to go about this? I have researched the algorithms that are common for this and the open source library opencv . Aside from rolling my own or using opencv is there an easy way to do this? I am open to any language or platform. Thanks If your background is consistend in an image but inconsistent across images it could get tricky, but here is what I would do: Separate the image into some intensity/colour form such as