I'm newbie with machine learning, and I have only basic knowledge in neural networks.
I have pretty clear task:
1. Video stream shows static picture (white area with yellow squares)
(in different videos squares located in different places)
2. In some moment content of the video changes, and starts to show white area without some of the yellow squares.
3. I need to create mechanism which can determines and somehow indicates that changes.
I'm going to use for that task TensorFlow framework. Could anybody push me in right direction? Or I'll be very happy to see list of steps to overcome the problem.
Thanks in advance.
If you know how the static picture looks beforehand, may be some background-subtraction would work? Basically you just subtract the static picture from every frame and check the content of the result. If the resulting picture is empty (zeros or close to it up to some threshold) there is no change to detect. If the resulting picture contains a region that is non-zero (may be above or below a certain manually tuned threshold), you detected a change in that region.
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Currently working on licentiate detection system and need some guidance on how to proceed.
I can capture (via video playback) and with the help of an open source library called OpenALPR display the license plates directly to the terminal, now the issue is it capture on a frame by frame basis so it capture the same license plate multiple times. I added a frame skip variable and now it skips however many number of frames I want it to but the issue is still there.
Furthermore, I'd like to distinguish between different license plates if possible but don't know how to work around that, I've attempted employing basic object detection and detection but failed miserably.
Below is an image of the program running, as seen it detects a single license plate and display multiple instance of it, now the issue is I expect it to move on to the next car and display Plate#1, unfortunately it does not and continues feeding into Plate #0
Program Running
Program Running
The function that actually helps display the license plate text is below, really the first line does all the work. OpenALPR is a pretty powerful.
results = alpr.recognize_ndarray(frame)
for i, plate in enumerate(results['results']):
best_candidate = plate['candidates'][0]
print('Plate #{}: {:} ({:}%)'.format(i,
best_candidate['plate'].upper(),
best_candidate['confidence']))
I'd like some guidance towards how I can solve this problem? Which is basically distinguish between different license plates.
It is a general problem without general solution, because it highly depends on context. Some thoughts:
If it is a video feed you can track the plate movement, the track will "jump" when it detects another plate. Let say the maximum optical flow velocity is 100 px/frame, if it jumps more than this threshold, you can suppose it is a new plate.
Depending on you video quality and detector, may there be spurious jumps, I would add a Kalman filter or any simple filter.
Perhaps there is a minimum time lapse between a plate goes out the image and the next arrives. You can use a time threshold to trigger the "changed plate alert" event.
I have multiple 1-minute videos taken outside (a building, a sea cliff, etc). The camera is not fixed, it's taken by a drone very slowly going up a cliff for example.
What I need to do, if even possible, is use OpenCV or other video processing framework to automatically add a path to each of those video, a red line.
The red line would follow the location pointed by the centre of the video frame.
It means that on each frame, I must find the locations pointed at on the previous/next frames via some sort of pattern recognition and somehow link them with an overlaid red line path.
Is there some sort of algorithm or tool that facilitate this process? How would you approach this problem?
Example:
From those frames:
*
*
*
*
To those frames:
(in the real, slow, footage, the consecutive frames would be much closer to each other)
Looks like for all frames, I must try to locate all the previous/next frames and link their centres as an rightly ordered line. Surely, that must have been done before?
I have the following image
What I need to do is connect the edges in MATLAB that are obviously of the same object in order to use regionprops later. By 'obviously' I mean the edges of the inside object and those of the outside one. What I thought is that I somehow must keep the pixels of each edge in a struct and then for each edge find the one that is closer to it and then apply some fitting(polynomial, bspline etc). The problem is that I have to make it for thousands of such images so I need a robust algorithm and I cannot do it by hand for all of them. Is there a way for somebody to help me? The image of which the previous image is obtained is this one. Ideally I have to catch the two interfaces shown there.
Thank you very much in advance
i am trying to subtract 2 image using the function cvAbsDiff(img1, img2, dest);
it working but sometimes when i bring my hand before my head or body the hand is not clear and background comes into picture... the background image(head) overlays my foreground.(hand)..
it works correctly on plain surfaces i.e when the background is even like a wall.
please check out my image...so that you can better understand my problem...!!!!
http://www.2shared.com/photo/hJghiq4b/bg_overlays_foreground.html
if you have any solution/hint please help me.......
There's nothing wrong with your code . Background subtraction is not a preffered way for motion detection or silhoutte detection because its not very robust.The problem is coming because both the background and the foreground are similar in colour at many regions which on subtractions pushes the foreground to back . You might try using
- optical flow for motion detection
- If your task is just detecting silhoutte or hand try training a HOG classifier over it
In case you do not want to try a new approach . You may try around playing with the threshold value(in your case 30).So when you subtract similar colour image there difference is less than 30 . And later you threshold with 30 so it just blacks out. Also you may try HSV or some other colourspace as well .
Putting in the relevant code would help. Also knowing what you're actually trying to achieve.
Which two images are you subtracting? I've done subtracting subsequent images (so, images taken with a delay of a fraction of a second), and the background subtraction generally results in the edges of moving objects, for example the edges of a hand, and not the entire silhouette of a hand. I'm guessing you're taking the difference of the current frame and a static startup frame. It's possible that parts aren't different enough (skin+skin).
I've got some computer problems tonight, I'll test it out tomorrow (pls put up at least the steps you actually carry thorough though) and let you know.
I'm still not sure what your ultimate goal is, although I'm guessing you want to do some gesture-recognition (since you have a vector called "fingers").
As Manpreet said, your biggest problem is robustness, and that is from the subjects having similar color.
I reproduced your image by having my face in the static comparison image, then moving it. If I started with only background, it was already much more robust and in anycase didn't display any "overlaying".
Quick fix is, make sure to have a clean subject-free static image.
Otherwise, you'll want to have dynamic comparison image, simplest would be comparing frame_n with frame_n-1. This will generally give you just the moving edges though, so if you want the entire silhouette you can either:
1) Use a different segmenting algorithm (what I recommend. Background subtraction is fast and you can use it to determine a much smaller ROI in which to search, and then use a different algorithm for more robust segmentation.)
2) Try to make a compromise between the static and dynamic comparison image, for example as an average of the past 10 frames or something like that. I don't know how well this works, but would be quite simple to implement, worth a try :).
Also, try with CV_THRESH_OTSU instead of 30 for your threshold value, see if you like that better.
Also, I noticed often the output flares (regions which haven't changed switch from black to white). Checking with the live stream, I'm quite certain it because of the webcam autofocusing/adjusting white balance etc.. If you're getting that too, turning off the autofocus etc. should help (which btw isn't done through openCV but depends on the camera. Possibly check this: How to programatically disable the auto-focus of a webcam?)
iam working in a project that i take a vedio by a camera and convert this vedio to frames (this part of project is done )
what iam facing now is how to detect moving object in these frames and differentiate them from the background so that i can distinguish between them ?
I recently read an awesome CodeProject article about this. It discusses several approaches to the problem and then walks you step by step through one of the solutions, with complete code. It's written at a very accessible level and should be enough to get you started.
One simple way to do this (if little noise is present, I recommend smoothing kernel thought) is to compute the absolute difference of two consecutive frames. You'll get an image of things that have "moved". The background needs to be pretty static in order to work. If you always get the abs diff from the current frame to the nth frame you'll have a grayscale image with the object that moved. The object has to be different from the background color or it will disappear...