I'm doing background subtraction using opencv. The problem is the foreground object is not always detected correctly. To deal with this I would like to use four or five images, and take their average as the background image. How can I do that?
Perhaps go through all the images, and if the pixel in question is within a certain range of colour variation for all the images, disregard it as background?
Then I suppose the size of the range would determine how picky you were and how confident you are in the stability and consistency of your camera.
You should try using the included background detector in OpenCV (under cvaux.h). They also have blob detector if you want to find object blob.
By combining blob information and optical flow information, you can usually find the foreground object.
Related
I'm looking for a program that will enable me to quantity the difference between images in an image sequence over time.
We are hoping to use timelapse images to measure the activity of tadpoles by comparing how the images change over time. Tracking the movement of individuals isn’t necessary. The tadpoles are dark and the background of the aquarium is light, however the background isn’t uniform and some of the decor items like dark rocks and foliage make it so that all the tadpoles aren’t visible at all times.
Basically need a program that will allow me to quantity the differences/motion detected in an image sequence (i.e 209 images) and produce data that can be exported...
Any and all suggestions appreciated!!
Your question is rather vague and you don't supply any images or real indication of what you expect as results, so my answer will not be as thorough as it might otherwise be.
You don't mention any tools you are familiar with, but my recommendation would be Python and OpenCV. Alternatives are probably scikit-image, Python Wand.
In general, when trying to detect movement across a series of images, you would:
try and work out what the background is
look for movement by sutracting, or differencing, frames from the background
clean up the difference image
identify objects - maybe by shape or size or colour
maybe track objects
produce statistics
As regards working out the background, I did an example here by finding the median pixel across all images at each location in the images. There is also an OpenCV tutorial here.
As regards cleaning up images, you can probably remove noise in the background subtraction with a small median filter, say 3x3 or 5x5 depending on the resolution of your images.
As regards detecting tadpoles, you will probably want to use OpenCV findContours() and filter by size, or colour, or circularity. There are some fairly decent tutorials on PyImageSearch. There is also an ImageMagick "Connected Component" analysis to find a tennis player that I did here.
I am trying to identify the best OpenCV methods and implementations to identify objects that appear in a live camera feed. The implementation needs to be robust to changing light conditions and would ideally accommodate slight movement in the background (trees/clouds moving) without picking up too much noise.
The options I have tried so far include;
-Identifying the absolute pixel differences between an empty background frame and the current frame (works poorly if light conditions change or if camera jiggles)
-Background subtraction (good for changing conditions but results in excessive noise)
-Have also thought about using edge detection, which would be unaffected by changing light conditions, and somehow compare object shapes to see what is new
I would ideally like an output that allows me to generate a bounding box for any objects that move around in the frame, with the background reference gradually changing over time to accommodate changing light conditions (or can a input frame be normalized for light so this has no effect?)
What would you recommend? I'm running OpenCV through Visual Studio 2017 in C++
I am doing course project on a similar project. I used several methods such as HSV/RGB threshold, Accumulative Difference Image, and MOG from OpenCV library.
For your case I would recommend Accumulative Difference Image which is very similar to MOG. Basically, you subtract consecutive frames from each other (previous from current), and then use threshold to convert to binary image. Then you can perform morphological operation on the binary image to enhance detection.
This method is relatively good for your case as it is not sensitive to reasonably slow changes of lighting and camera movements. However stationary object will not be detected.
In order to remove the noise you may want to Erode and then Dilate the binary image (play around with morphological operations).
Good luck!
I am trying to subtract two images using absdiff function ,to extract moving object, it works good but sometimes background appears in front of foreground.
This actually happens when the background and foreground colors are similar,Is there any solution to overcome this problem?
It may be description of the problem above not enough; so I attach images in the following
link .
Thanks..
You can use some pre-processing techniques like edge detection and some contrast stretching algorithm, which will give you some extra information for subtracting the image. Since color is same but new object should have texture feature like edge; if the edge gets preserved properly then when performing image subtraction you will obtain the object.
Process flow:
Use edge detection algorithm.
Contrast stretching algorithm(like histogram stretching).
Use the detected edge top of the contrast stretched image.
Now use the image subtraction algorithm from OpenCV.
There isn't enough information to formulate a complete solution to your problem but there are some tips I can offer:
First, prefilter the input and background images using a strong
median (or gaussian) filter. This will make your results much more
robust to image noise and confusion from minor, non-essential detail
(like the horizontal lines of your background image). Unless you want
to detect a single moving strand of hair, you don't need to process
the raw pixels.
Next, take the advice offered in the comments to test all 3 color
channels as opposed to going straight to grayscale.
Then create a grayscale image from the the max of the 3 absdiffs done
on each channel.
Then perform your closing and opening procedure.
I don't know your requirements so I can't take them into account. If accuracy is of the utmost importance. I'd use the median filter on input image over gaussian. If speed is an issue I'd scale down the input images for processing by at least half, then scale the result up again. If the camera is in a fixed position and you have a pre-calibrated background, then the current naive difference method should work. If the system has to determine movement from a real world environment over an extended period of time (moving shadows, plants, vehicles, weather, etc) then a rolling average (or gaussian) background model will work better. If the camera is moving you will need to do a lot more processing, probably some optical flow and/or fourier transform tests. All of these things need to be considered to provide the best solution for the application.
I am doing some image processing of the retina images.. I need to replace the blood vessels with background pixels so that I can focus on other aspects of retina. I could not figure out a way to do this. I am using matlab. any suggestions?
Having worked extensively with retinal images, I can tell you that what you're proposing is a complex problem in itself. Sure, if you just want a crude method, you can use imdilate. But that will affect your entire image, and other structures in the image will change appearance. Something, that is not desirable.
However, if you want to do it properly, you will first need to segment all the blood vessels and create a binary mask. Once you have a binary mask, it's up to you how to fill up the vessel regions. You can either interpolate from the boundaries or calculate a background image and replace the vessel regions with pixels from the background image, etc.
Segmentation of the blood vessels is a challenging problem and you will find a lot of literature concerning that on the internet. Ultimately, you will have to choose how accurate a segmentation you want and build your algorithm accordingly.
imdilate should do what you want, since it replaces each pixel with the maximum of its neighbors. For more detailed suggestions, I'd need to see images.
I am working on a project that requires me to:
Look at images that contain relatively well-defined objects, e.g.
and pick out the color of n-most (it's generic, could be 1,2,3, etc...) prominent objects in some space (whether it be RGB, HSV, whatever) and return it.
I am looking into ways to segment images like this into the independent objects. Once that's done, I'm under the impression that it won't be particularly difficult to find the contours of the segments and analyze them for average or centroid color, etc...
I looked briefly into the Watershed algorithm, which seems like it could work, but I was unsure of how to generate the marker image for an indeterminate number of blobs.
What's the best way to segment such an image, and if it's using Watershed, what's the best way to generate the corresponding marker image of integers?
Check out this possible approach:
Efficient Graph-Based Image Segmentation
Pedro F. Felzenszwalb and Daniel P. Huttenlocher
Here's what it looks like on your image:
I'm not an expert but I really don't see how the Watershed algorithm can be very useful to your segmentation problem.
From my limited experience/exposure to this kind of problems, I would think that the way to go would be to try a sliding-windows approach to segmentation. Basically this entails walking the image using a window of a set size, and attempting to determine if the window encompasses background vs. an object. You will want to try different window sizes and steps.
Doing this should allow you to detect the object in the image, presuming that the images contain relatively well defined objects. You might also attempt to perform segmentation after converting the image to black and white with a certain threshold the gives good separation of background vs. objects.
Once you've identified the object(s) via the sliding window you can attempt to determine the most prominent color using one of the methods you mentioned.
UPDATE
Based on your comment, here's another potential approach that might work for you:
If you believe the objects will have mostly uniform color you might attempt to process the image to:
remove noise;
map original image to reduced color space (i.e. 256 or event 16 colors)
detect connected components based on pixel color and determine which ones are large enough
You might also benefit from re-sampling the image to lower resolution (i.e. if the image is 1024 x 768 you might reduce it to 256 x 192) to help speed up the algorithm.
The only thing left to do would be to determine which component is the background. This is where it might make sense to also attempt to do the background removal by converting to black/white with a certain threshold.