I am using OpenCV to process some videos where a user is placing their hands on different parts of a wall. I've selected some regions of interest and I'm currently just using cv2.absdiff on the original image of the wall with no user and the current frame to detect whether the user has their hand in a region of interest by looking at the average pixel difference. If it's above some threshold, I consider that region "activated".
The problem I'm having is that some of the video clips contain lighting and positions that result in the user casting a shadow over certain ROIs, such that they are above the threshold. Is there a good way to filter out shadows when diffing images?
OpenCV has a Mixture of Gaussian based background subtractor which also has an option to account for shadow. You can use this instead of absdiff. MOG can be a bit slow though, compared to absdiff.
Alternatively, you can convert to HSV, and check that the Hue doesn't change.
You could first detect shadow regions in the original images, and exclude them from the difference imaging part. This paper provides a simple but effective method to detect shadows in images. They explore a colour space that is invariant to shadows.
Related
I've got a video stream from the camera. My goal is to detect and track position of moving object (train).
First of all I tried to use movement detection (frames difference, background subtractors) but it gave bad results.
Tried to cling to the color of the object but it's often (bad lighting, blurry) the same color as a ground (railways).
So the current approach is to divide the area of movement of object into n small regions and define difference between the stored region when there's no object and current one.
The main problem in here is that lightness is changing and when I use a stored region from a reference frame (where there's no object) the brightness of the current frame might be different and it breaks it up.
Also brightness can change while object moving.
It helps me to apply a gaussianBlur and histogramEqualization to make sensitivity to changes in brightness a bit less.
I tried to compare a structure of according regions using ssim, lbph, hog. When I test lbph and hog approaches on the manual cropped regions which is larger than real ones it looked like working but then I used them for my small regions and it stopped working.
In the moment the most efficient approach is just difference between grayscale regions with rmse using a fixed thresholds but it's not robust approach and it suffers a lot when brightness is changing.
Now I'm trying to use some high-pass operator to extract the most dominant edges like with sobel operator in the attached figure but I'm not sure how to properly compare the high-passed regions except by finding the difference.
Frame with an empty railway:
In some seconds a train is appeared and luminance was changed.
At night time luminance is also different.
So the questions are:
What approaches are there for comparison of high-passed images?
Is there any other way to determine if an area is overlapped which you could suggest me?
I need to track cars on the road from top-view video.
My application contain two main parts:
Detecting cars on the frame (Tensorflow trained network)
Tracking detected cars (opencv trackers)
I have troubles with opencv trackers. Initially i tried to different trackers, but only MOSSE is fast enough. This tracker works almost perfect for case with straight road, but i faced problems with rotating cars. This situation appears on crossroads.
As i understood, bounding box of rotated object is bigger that bbox of horizontal or vertical object. As result bbox contains big part of static background and the tracker lose target object.
Are there any alternative trackers which can track contours (not bounding boxes)?
Can i adjust quality of existing opencv trackers results by any settings or by adjusting picture?
Schema:
Real image:
If your camera is stationary the following scenario is feasible:
use ‌background subtraction methods to separate background image from foreground blobs.
Improve the foreground results using morphological operations.
Detect car blobs and remove other blobs.
Track foreground blobs in video i.e. binary track (simply use this or even apply KF).
A very basic but effective approach in this scenario might be to track the center coordinates of the bounding box, if the center coordinates only change along one axis (with a small tolerance for either axis), its a linear motion (not a rotation). If both x and y change, the car is moving in the roundabout.
This only has the weakness that it will detect diagonal motion, but since you are looking at a centered roundabout, that shouldn't be an issue.
It will also be very efficient memory-wise.
You should use PCA method, which can calculate the orientation of an detected object and which way it is facing. You can change the threshold of detection to select objects more like the cars (based upon shape and colour - a HSV conversion which in your case is red) in your picture.
Link to an introduction to Principal Component Analysis (PCA)
Method 1 :
- Detect bounding boxes and subtract the background to get blobs rotated rectangles.
Method 2 :
- implement your own version of detector with rotated boxes.
Method 3 :
- Use segmentation instead ... Unet for example.
There are no other trackers than the ones found in the library.
Your best bet is to filter the image and use findcontours.
Optical flow and background subtraction will help with this. You can combine optical flow with your car detector to rule out false positives.
https://docs.opencv.org/3.4/d4/dee/tutorial_optical_flow.html
https://docs.opencv.org/3.4/d1/dc5/tutorial_background_subtraction.html
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!
We are planning to create a surface damage detection prototype for ceramic tiles with surface discoloration as a specific damage through the use of OpenCV. We would like to know what method should we consider using. We are new into developing these types of object recognition/object tracking programs. We've read about methods such as the Histogram method and the one where the Hue saturation value was being tracked, but still we are confused.
Also, we would like to know whether it is possible to detect the Hue saturation value of an object without the use of track bars.
Any relevant and helpful response will be greatly appreciated.
I think you can do it in sequence:
1) find tile region. Use corners detector, hough lines, etc.
2) find SIFT (or other descriprors) and recognize what image must be on this tile (find it in you tiles images database).
3) align images carefully. For example find homograpy between found in DB image and image of tile from camera (using SIFT features).
4) find color distance between every pixel in tile image from camera and tile image from database.
5) threshold differences by some value -> get problematic regions
And think about lighting. You have to provide equal lighting conditions for you measurements.
The objective is to display the person on a different background (aka background removal).
I'm using the Kinect with Microsoft's Beta Kinect SDK to do so. With help of the depth, the background is filtered and we get only the image of the person.
This is pretty simple to do, and we can find the code that does that everywhere on the Internet. However, the depth signal is noisy, and we get pixels which do not belong to the person that are displayed.
I applied an edge detector to see if it was useful, and I currently get this:
Here's another without edge detection:
My question is: Which way can I get rid of these noisy white pixels around the person?
I tried morphological operations, but some parts of the body are erased and still leave white pixels behind.
The algorithm doesn't need to be real-time, I can just apply it when I press a 'Save image' button.
Edit 1:
I just tried to do background substraction with the closest frames on the shape border. The single pixels you see are flickering, which means it is noise and I can get easily get rid of them.
Edit 2:
The project is now over, and here's what we did: manual calibration of the Kinect by using the OpenNI driver, which provides directly the infrared image. The result is really good, but each calibration is specific to each Kinect.
Then, we applied a little transparency on the borders, and the result looks really nice! I can't provide pictures, however.
Your problem isn't just the noisy white pixels. You're missing significant parts of the person as well, e.g. part of his right hand. I'd recommend being more conservative with your thresholding of the depth data (allow more false positives). This would give you more noisy pixels, but at least you'd have the person in their entirety.
To get rid of the noisy pixels, I can think of a couple of things:
Feather the outer pixels (reduce them in intensity/increase their transparency if you're using an alpha channel)
Smooth the image, perform the edge detection on the smoothed image, then use these edges with your original sharp image.
Do some skin region detection to mark parts that definitely belong to a person. See skin detection in the YUV color space? and Skin Color Detection
For clothes, work with the hue and saturation image. If you know the color of the t-shirt (or that at least that it's not a neutral color), then this will stand out easily. If you don't know this information, then it may be worth building up a model of the person using the other frames (if there's a big gray blob that's moving around in your video, chances are that your subject is wearing a gray shirt)
The approaches aren't mutually exclusive so it may be worth trying to do them in combination. If I think of anything else, I'll post back here.
If there is no other way of resolving the jitter on the edges you could always try anti-alias as post-process.