I am attempting to use a straightforward motion detection code to detect movement from a camera. I'm using the OpenCV library and I have some code that takes the difference between two frames to detect a change.
I have the difference frame working just fine and it's black when no motion is present.
The problem is how now i can detect that blackness to stop recording or no darkness to begin recording frames.
Thank u all.
A very simple thing to do is to sum the entire diff image into an integer. If that sum is above a threshold you have movement. Then you can use a second threshold and when the sum is below that limit you stopped having movement.
You can also make the threshold only change the program state if some elapsed time has occurred since the last threshold. i.e. after movement is detected you don't check for lack of movement for 10 seconds.
Take a look at the code of the free software motion for getting inspiring ideas.
There are quite a few things to keep in mind for reliable motion detection. For example tolerate the slow changes from the sun's rotation. Or accepting momentary image glitches which can come especially from the cheapest cameras.
From a small experience I have had, I think that better than just adding up all differences, it works better to count the number of pixels whose variation exceeds a certain threshold.
Motion also offers masks, which let you for example ignore movements in a nearby road.
What about storing a black frame internally and using your same comparison code? If your new frame is different (above a threshold) from the all-black frame, start recording.
This seems the most straightforward since you already have the image-processing algorithms down.
Related
When running mono Slam how is scale consistency achieved between frames? One short tutorial ran the 5point algorithm repeatedly and used a motion model for consistency of scale between frames but that is clearly not done in the general case.
I think I read something once about how the 5 point algorithm is used initially to estimate motion between first two frames then features are tracked over time using reprojection loss and 3d projection.
How is it done?
I am trying to improve my webcam based OpenCV mouse controller for disabled people (MFC C++ application): https://preability.com/face-controlled-mouse/
The cursor moves, when a person moves her/his head, clicks when smile, etc.
Controller finds face area then use goodFeaturesToTrack, cornerSubPix and calcOpticalFlowPyrLK.
In general, I managed to stabilize cursor if lighting is good.
What I use now:
Evaluating and filtering the direction of the each corner point movement.
Spreading the corner points all over the face area for cv::goodFeaturesToTrack() helped a little too.
EWMA (or Kalman) filter for the cursor position.
I’ve included equalizeHist() for the face ROI. The detector performed much better in low light conditions.
In addition, I tried morphology operations of OpenCV without improvement.
However, the corner points still dance in uneven lighting.
I can see that similar old program eViacam has preprocessing module for webcam Creavision (old too) and the corner points are more stable.
Please advise what can be done with the input Mat? Or how can the video be processed with reasonable CPU loading?
Now I can answer my own question. Christoph Rackwitz gave me some good advice:
don’t track the whole head. track each feature. and don’t use those trackers, they’re too complex. use MOSSE. it’s dumb but very precise, as long as the object (which should be a tiny feature on the face) doesn’t change much.
MOSSE approaches optical flow. methods to calculate optical flow work like MOSSE, except they use simpler math and smaller regions, hence the result is noisier. MOSSE uses a larger area (for a single track/point of course) and more sophisticated math, for a more accurate result.
When MOSSE algorithm tracks “corner points”, cursor moves much more smoothly. There was a slight issue with discrete movement as the object rectangles moved the same number of pixels at the same time. Cursor moved in leaps. So, I had to use filter on each tracked point. Anyway, as you can see in the video, the CPU load did not increase comparing to Lukas-Kanade optical flow algorithm + filtration only cursor position. In good light, the difference is also very noticeable.
https://www.youtube.com/watch?v=WKwuas0GVkA
Lucas-Kanade optical flow:
goodFeaturesToTrack,
cornerSubPix,
calcOpticalFlowPyrLK,
cursor EWMA filter
MOSSE object tracking:
goodFeaturesToTrack,
cornerSubPix,
TrackerMOSSE,
all points EWMA filtration
And of course I had to remember to include the tracking453.lib to Linker when add legacy Tracker. I spent half a day googling the “unresolved external symbol LNK2001 error”. For some reason including a tracker from the core library (cv::Tracker) does not result such compilation error, so it is confusing.
I have a custom USB camera with a custom driver on a custom board Nvidia Jetson TX2 that is not detected through openpose examples. I access the data using GStreamer custom source. I currently pull frames into a CV mat, color convert them and feed into OpenPose on a per picture basis, it works fine but 30 - 40% slower than a comparable video stream from a plug and play camera. I would like to explore things like tracking that is available for streams since Im trying to maximize the fps. I believe the stream feed is superior due to better (continuous) use of the GPU.
In particular the speedup would come at confidence expense and would be addressed later. 1 frame goes through pose estimation and 3 - 4 subsequent frames are just tracking the object with decreasing confidence levels. I tried that on a plug and play camera and openpose example and the results were somewhat satisfactory.
The point where I stumbled is that I can put the video stream into CV VideoCapture but I do not know, however, how to provide the CV video capture to OpenPose for processing.
If there is a better way to do it, I am happy to try different things but the bottom line is that the custom camera stays (I know ;/). Solutions to the issue described or different ideas are welcome.
Things I already tried:
Lower resolution of the camera (the camera crops below certain res instead of binning so cant really go below 1920x1080, its a 40+ MegaPixel video camera by the way)
use CUDA to shrink the image before feeding it to OpenPose (the shrink + pose estimation time was virtually equivalent to the pose estimation on the original image)
since the camera view is static, check for changes between frames, crop the image down to the area that changed and run pose estimation on that section (10% speedup, high risk of missing something)
First of all, I understand this question has been asked several times at here.
FindChessboardCorners cannot detect chessboard on very large images by long focal length lens
Opencv corners detection for High resolution images
However, my situation is a little bit different.
My first experiment is retrieving sequential images of 3264 x 2448 from a Webcam which supports resolution this high, and uses findChessboardCorners to detect the corners on a pattern I placed.
Gladly it works! So I move to next experiment.(See success cases below, I cropped them)
This time I try to project a pattern from a projector of my own to a clean board and detect it, Sadly I failed at here.(Example below, 2592 x 1944)
The two experiments retrieves similar images(I think so), but how come one succeeds and the other one won't? Especially the succeed one has the highest resolution.
I also tried adjust the size of the pattern that projector projects, didn't work.
Adjust the distance of the board, didn't work.
Adjust the camera settings, from lighter to darker, didn't work.
By the way, I suppose the resolution I choose affects the camera intrinsic parameters, so "resize" the image shouldn't be a good idea right? Since I require parameters under high resolution.
I know the title is a bit vague but I'm not sure how else to describe it.
CentOS with ffmpeg + OpenCV 2.4.9. I'm working on a simple motion detection system which uses a stream from an IP camera (h264).
Once in a while the stream hiccups and throws in a "bad frame" (see pic-bad.png link below). The problem is, these frames vary largely from the previous frames and causes a "motion" event to get triggered even though no actual motion occured.
The pictures below will explain the problem.
Good frame (motion captured):
Bad frame (no motion, just a broken frame):
The bad frame gets caught randomly. I guess I can make a bad frame detector by analyzing (looping) through the pixels going down from a certain position to see if they are all the same, but I'm wondering if there is any other, more efficient, "by the book" approach to detecting these types of bad frames and just skipping over them.
Thank You!
EDIT UPDATE:
The frame is grabbed using a C++ motion detection program via cvQueryFrame(camera); so I do not directly interface with ffmpeg, OpenCV does it on the backend. I'm using the latest version of ffmpeg compiled from git source. All of the libraries are also up to date (h264, etc, all downloaded and compiled yesterday). The data is coming from an RTSP stream (ffserver). I've tested over multiple cameras (dahua 1 - 3 MP models) and the frame glitch is pretty persistent across all of them, although it doesn't happen continuously, just once on a while (ex: once every 10 minutes).
What comes to my mind in first approach is to check dissimilarity between example of valid frame and the one we are checking by counting the pixels that are not the same. Dividing this number by the area we get percentage which measures dissimilarity. I would guess above 0.5 we can say that tested frame is invalid because it differs too much from the example of valid one.
This assumption is only appropriate if you have a static camera (it does not move) and the objects which can move in front of it are not in the shortest distance (depends from focal length, but if you have e.g. wide lenses so objects should not appear less than 30 cm in front of camera to prevent situation that objects "jumps" into a frame from nowhere and has it size bigger that 50% of frame area).
Here you have opencv function which does what I said. In fact you can adjust dissimilarity coefficient more large if you think motion changes will be more rapid. Please notice that first parameter should be an example of valid frame.
bool IsBadFrame(const cv::Mat &goodFrame, const cv::Mat &nextFrame) {
// assert(goodFrame.size() == nextFrame.size())
cv::Mat g, g2;
cv::cvtColor(goodFrame, g, CV_BGR2GRAY);
cv::cvtColor(nextFrame, g2, CV_BGR2GRAY);
cv::Mat diff = g2 != g;
float similarity = (float)cv::countNonZero(diff) / (goodFrame.size().height * goodFrame.size().width);
return similarity > 0.5f;
}
You do not mention if you use ffmpeg command line or libraries, but in the latter case you can check the bad frame flag (I forgot its exact description) and simply ignore those frames.
remove waitKey(50) or change it to waitKey(1). I think opencv does not spawn a new thread to perform capture. so when there is a pause, it confuses the buffer management routines, causing bad frames..maybe?
I have dahua cameras and observed that with higher delay, bad frames are observed. And they go away completely with waitKey(1). The pause does not necessarily need to come from waitKey. Calling routines also cause such pauses and result in bad frames if they are taking long enough.
This means that there should be minimum pause between consecutive frame grabs.the solution would be to use two threads to perform capture and processing separately.