I have a video of soccer in which the players are relatively far away from the camera and thus represent small portions of the image. I'm using background subtraction to detect the players and the results are fine but I have been asked to try detecting using Hog.
I tried using the detect MultiScale using the default descriptors presented on opencv but i cant get any detection. I dont really understand how can I make it work on this case, because on other sequences where the people are closer to the camera, the detector works fine.
Here is a sample image link
Thanks.
The descriptor you use with HOG determines the minimum size of person you can detect: with the DefaultPeopleDetector the detection window is 128 pixels high x 64 wide, so you can detect people around 90px high. With the Daimler descriptor the size you can detect is a bit smaller.
Your pedestrians are still too small for this, so you may need to magnify the whole image, or just the parts which show up as foreground using background segmentation.
Have a look at the function definition for detectMultiscale http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html#cascadeclassifier-detectmultiscale
It might be that you need to reduced the value of minsize so as to detect smaller people or the people might just be too far away.
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've written an app with an object detection model and process images when an object is detected. The problem I'm running into is when an object is detected with 99% confidence but the frame I'm processing is very blurry.
I've considered analyzing the frame and attempting to detect blurriness or detecting device movement and not analyzing frames when the device is moving a lot.
Do you have any other suggestions to only process un-blurry photos or solutions other than the ones I've proposed? Thanks
You might have issues detecting "movement" when for instance driving in car. In that case looking at something inside your car is not considered as movement while looking at something outside is (if it's not far away anyway). There can be many other cases for this.
I would start by checking if camera is in focus. It is not the same as checking if frame is blurry but it might be very close.
The other option I can think of is simply checking 2 or more sequential frames and see if they are relatively the same. To do something like that it is bast to define a grid for instance 16x16 on which you evaluate similar values. You would need to mipmap your photos which manually means resizing it by half till you get to 16x16 image (2000x1500 would become 1024x1024 -> 512x512 -> 256x256 ...). Then grab those 16x16 pixels and store them. Once you have enough frames (at least 2) you can start comparing these values. GPU is perfect for resizing but those 16x16 values are probably best evaluated on the CPU. What you need to do is basically find an average pixel difference in 2 sequential 16x16 buffers. Then use that to evaluate if detection should be enabled.
This procedure may still not be perfect but it should be relatively feasible from performance perspective. There may be some shortcuts as some tools maybe already do resizing so that you don't need to "halve" them manually. From theoretical perspective you are creating sectors and compute their average color. If all the sectors have almost same color between 2 or more frames there is a high chance the camera did not move in that time much and the image should not be blurry from movement. Still if camera is not in focus you can have multiple sequential frames that are exactly the same but in fact they are all blurry. Same happens if you detect phone movement.
I'm currently working on a project where I need to be able to very reliable get the positions of the balls on a pool table.
I'm using a Kinect v2 above the table as the source.
Initial image looks like this (after converting it to 8-bit from 16-bit by throwing away pixels which is not around table level):
Then a I subtract a reference image with the empty table from the current image.
After thresholding and equalization it looks like this: image
It's fairly easy to detect the individual balls on a single image, the problem is that I have to do it constantly with 30fps.
Difficulties:
Low resolution image (512*424), a ball is around 4-5 pixel in diameter
Kinect depth image has a lot of noise from this distance (2 meters)
Balls look different on the depth image, for example the black ball is kind of inverted compared to the others
If they touch each other then they can become one blob on the image, if I try to separate them with depth thresholding (only using the top of the balls) then some of the balls can disappear from the image
It's really important that anything other than balls should not be detected e.g.: cue, hands etc...
My process which kind of works but not reliable enough:
16bit to 8bit by thresholding
Subtracting sample image with empty table
Cropping
Thresholding
Equalizing
Eroding
Dilating
Binary threshold
Contour finder
Some further algorithms on the output coordinates
The problem is that a pool cue or hand can be detected as a ball and also if two ball touches then it can cause issues. Also tried with hough circles but with even less success. (Works nicely if the Kinect is closer but then it cant cover the whole table)
Any clues would be much appreciated.
Expanding comments above:
I recommend improving the IRL setup as much as possible.
Most of the time it's easier to ensure a reliable setup than to try to "fix" that user computer vision before even getting to detecting/tracking anything.
My suggestions are:
Move the camera closer to the table. (the image you posted can be 117% bigger and still cover the pockets)
Align the camera to be perfectly perpendicular to the table (and ensure the sensor stand is sturdy and well fixed): it will be easier to process a perfect top down view than a slightly tilted view (which is what the depth gradient shows). (sure the data can be rotated, but why waste CPU cycles when you can simply keep the sensor straight)
With a more reliable setup you should be able to threshold based on depth.
You can possible threshold to the centre of balls since the information bellow is occluded anyway. The balls do not deform, so it the radius decreases fast the ball probably went in a pocket.
One you have a clear threshold image, you can findContours() and minEnclosingCircle(). Additionally you should contrain the result based on min and max radius values to avoid other objects that may be in the view (hands, pool cues, etc.). Also have a look at moments() and be sure to read Adrian's excellent Ball Tracking with OpenCV article
It's using Python, but you should be able to find OpenCV equivalent call for the language you use.
In terms tracking
If you use OpenCV 2.4 you should look into OpenCV 2.4's tracking algorithms (such as Lucas-Kanade).
If you already use OpenCV 3.0, it has it's own list of contributed tracking algorithms (such as TLD).
I recommend starting with Moments first: use the simplest and least computationally expensive setup initially and see how robuts the results are before going into the more complex algorithms (which will take to understand and get the parameters right to get expected results out of)
I'm doing real-time frame-by-frame analysis of a video stream in iOS.
I need to assign a score to each frame for how in focus it is. The method must be very fast to calculate on a mobile device and should be fairly reliable.
I've tried simple things like summing after using an edge detector, but haven't been impressed by the results. I've also tried using the focus scores provided in the frame's metadata dictionary, but they're significantly affected by the brightness of the image, and much more device-specific.
What are good ways to calculate a fast, reliable focus score?
Poor focus means that edges are not very sharp, and small details are lost. High JPEG compression gives very similar distortions.
Compress a copy of your image heavily, unpack and calculate the difference with the original. Intense difference, even at few spots, should mean that the source image had sharp details that are lost in compression. If difference is relatively small everywhere, the source was already fuzzy.
The method can be easily tried in an image editor. (No, I did not yet try it.) Hopefully iPhone has an optimized JPEG compressor already.
A simple answer that human visual system probably uses is to implemnt focusing on top of edge
Tracking. Thus if a set of edges can be tracked across a visual sequence one can work with intensity profile
Of these edges only to detrmine when it the steepest.
From a theoretical point of view, blur manifests as a lost of the high frequency content. Thus, you can just use do a FFT and check the relative frequency distribution. iPhone uses ARM Cortex chips which have NEON instructions that can be used for an efficient FFT implementation.
#9000's suggestion of heavily compressed JPEG has the effect of taking a very small number of the largest wavelet coefficients will usually result in what's in essence a low pass filter.
Consider different kind of edges: e.g. peaks versus step edges. The latter will still be present regardless of focus. To isolate the former use non max suppression in the direction of gradient. As a focus score use the ratio of suppressed edges at two different resolutions.
I have a video feed. This video feed contains several lights blinking at different rates. All lights are the same color (they are all infrared LEDs). How can I detect the position and frequency of these blinking lights?
Disclaimer: I am extremely new to OpenCV. I do have a copy of Learning OpenCV, but I am finding it a bit overwhelming. If anyone could explain a solution in OpenCV terminology, it would be greatly appreciated. I am not expecting code to be written for me.
Threshold each image in the sequence with a threshold that makes the LED:s visible. If you can threshold it with a threshold that only keeps the LED and removes background then you are more or less finished since all you need to do now is to keep track of each position that has seen a LED and count how often it occurs.
As a middle step, if there is "background noise" in the thresholded image would be to use erosion to remove small mistakes, and then maybe dilate to "close holes" in the blobs you are actually interested in.
If the scene is static you could also make a simple background model by taking the median of a few frames and removing the resulting median image from any frame and threshold that. Stuff that has changed (your LEDs) will appear stronger.
If the scene is moving I see no other (easy) solution than making sure the LED are bright enough to be able to use the threshold approach given above.
As for OpenCV: if you know what you want to do, it is not very hard to find a function that does it. The hard part is coming up with a method to solve the problem, not the actual coding.
If the leds are stationary, the problem is far simpler than when they are moving. Assuming they are stationary, a solution to find the frequency could simply be to keep a vector or an array for each pixel location in which you store the values of that pixel, preferably after the preprocessing described by kigurai, over some timeframe. You can then compute the 1D fourier transform of those value vectors and find the ground frequency as the first significant component after the DC peak. If the DC peak is too low, it means there is no led there.
Hope this problem is still somewhat actual, and that my solution makes sense.