i am working on a project detecting and tracking fingers. Though i find there is quiet a lot resource on this task, i haven't found a effective one yet :(.
So far i have thought of methods to detect hands as follow:
Haar training. But firstly we don't have a trained set(xml) as that in the face detection. Secondly, if we do the training ourselves, we don't have enough samples (i am still a college student)
skin color detection in HSV space. I have tried this one but the result has a lot of noises so cannot helps me continue the further detection on fingertip.
3.use Handvu. But i have heart that this lib is hard to set up and used in windows...
So in a word, can anyone give me any suggestions on how to detect hands effectively? (After that i may consider about detecting fingertips..)
Thanks!!
Here is a pretty in-depth paper on finger segmentation using Zernike moments. Here is a good paper on using Zernike moments for image recognition as a basis for the first paper.
Can you explain more about your experimental setup? Are you trying to track fingers against a cluttered background, or a plain cardboard sheet?
Haar like features perform very well for face recognition (the Viola Jones paper being a classic example) however I would not recommend them for your task. Although they can be computed fast using the integral image, they work well using a CASCADED Adaboost classification framework.
For skin colour detection, it depends on your setup. As a first step you could try doing background subtraction: simply learn the distribution (histogram) of pixels for foreground (ie. the hand) and the background and use these to do image segmentation.
I don't know what Handvu is
Zernike moments are also very good shape descriptors that are rotation invariant and can be made to be both scale and translation invariant.
I hope this helps!
Related
So I want to count how many people appear in a facebook profile picture.
Typically there are 0-2 people (sometimes there are 4-5+ but that's more rare).
A sample dataset (and a few tries using python) can be found here:
https://github.com/yoniker/FaceDetect
I've tried different methods, none of them give reasonable results (all of those methods are wrong most of the time),I've tried the following:
-Face detection- http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html
It usually doesn't find anyone (that happens at around 75% of the pictures)- and I have tried different Haar filters and parameters.
-Pedestrian Detection http://www.pyimagesearch.com/2015/11/09/pedestrian-detection-opencv/
Again it doesn't find people most of the time.
OpenFace:Probably this face recognition algo doesn't truly help with face detection (see https://groups.google.com/forum/#!topic/cmu-openface/X6erXKckk0Q).
And finally I've looked at different StackOverflow questions such as
Count the number of people in the video but none of them are relevant!
I've tried for half a day now- so help will be super appreciated!!
For me, dlib has given better results than using OpenCV's haar face detector. It has python bindings too. You can find quick-start code to do face detection here.
It would be possible to help better if you post an Image in which faces are not detected properly.
Having said that, to improve face detection apart from using dlib, you can experiment with these ideas:
Use histogram equalisation(equalizeHist on opencv) on gray scale image before passing it to face detector. (i.e preprocess your images)
If faces are tilted to left or right, more often face detection fails. To solve this rotate the images in steps of 5 degrees upto 30 degrees and apply face detection. At each rotation you might detect new faces.
Most face detectors which are not using deep learning detect mostly frontal faces. Not much could be done about this apart from using deep learning or train your own side profile face detector using HOG or HAAR features.
Hope this helps you to improve your face detection.
There's always the cascade classifier in OpenCV for all your face detection needs. If you could feed it with some nice features it would give you all the results.
For a project of mine, I'm required to process images differences with OpenCV. The goal is to detect an intrusion in a zone.
To be a little more clear, here are the inputs and outputs:
Inputs:
An image of reference
A second image from approximately the same point of view (can be an error margin)
Outputs:
Detection of new objects in the scene.
Bonus:
Recognition of those objects.
For me, the most difficult part of it is to take off small differences (luminosity, camera position margin error, movement of trees...)
I already read a lot about OpenCV image processing (subtraction, erosion, threshold, SIFT, SURF...) and have some good results.
What I would like is a list of steps you think is the best to have a good detection (humans, cars...), and the algorithms to do each step.
Many thanks for your help.
Track-by-Detect, human tracker:
You apply the Hog detector to detect humans.
You draw a respective rectangle as foreground area on the foreground mask.
You pass this mask to "The OpenCV Video Surveillance / Blob Tracker Facility"
You can, now, group the passing humans based on their blob.{x,y} values into public/restricted areas.
I had to deal with this problem the last year.
I suggest an adaptive background-foreground estimation algorithm which produced a foreground mask.
On top of that, you add a blob detector and tracker, and then calculate if an intersection takes place between the blobs and your intrusion area.
Opencv comes has samples of all of these within the legacy code. Ofcourse, if you want you can also use your own or other versions of these.
Links:
http://opencv.willowgarage.com/wiki/VideoSurveillance
http://experienceopencv.blogspot.gr/2011/03/blob-tracking-video-surveillance-demo.html
I would definitely start with a running average background subtraction if the camera is static. Then you can use findContours() to find the intruding object's location and size. If you want to detect humans that are walking around in a scene, I would recommend looking at using the built-in haar classifier:
http://docs.opencv.org/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.html#cascade-classifier
where you would just replace the xml with the upperbody classifier.
I'm working on an application which needs to detect the location of a face in a video stream, using a web cam placed at desk height (and slightly off to the side of the user).
I've already implemented a version of OpenCV (using their Haar detection) and it works ok... the problem is that it tends to lose the position of the face if the user turns their head to the side (or looks up).
Since the webcam is sitting on the desk, it is tilted up at a 30 degree angle. The OpenCV detection algorithm is trained using fully frontal images, but not up-angle images like the ones I'm using. I know OpenCV also has a profile Haar file that can be used.. but from my research it seems that the results are quite mixed on profile detection. In addition, I don't really have control over the background or lighting of the image... so this sometimes also effects the efficacy of the OpenCV detection algorithm.
So, I guess what I'm asking is... are there other face detection algorithms (that are hopefully free, as this is part of my university research) that are better for detecting faces for this type of setup? It seems like some of the built-in webcams (for Macs and PCs) actually have fairly robust algorithms for detecting faces (and then overlaying cheesy cartoon images over the faces)... but they seem to work well regardless of background or lighting. Do you have any recommendations?
Thanks.
For research purposes, you can use the Haar cascades in OpenCV, things are different if you want to go commercial (in which case you need to consider LBP cascades instead). Just be sure to quote the Viola-Jones paper in your references.
To improve the results of face detection, you have several paths:
individual image detection: you can send rotated images to a frontal cascade to account for some variability without training your own cascade
individual image detection but more work) : train your own cascade in operating conditions closer to the ones of your app
stability in video streams (as in webcams & co.) : this is achieved by adding a layer of tracking around the face detection. Depending on your knowledge about this topic, you can use your own filter, have fun with OpenCV's particle or Kalman filter, implement a simple first or second order low pass filter on the face position or a PID tracker on the detected face...
Any of these tracking filters will enhance a lot your results when processing video streams.
Use CLM-framework for accurate realtime face detection and face landmark detection.
Example of the system in action: http://youtu.be/V7rV0uy7heQ
You may find it useful.
I'm attempting to implement an easter egg in a mobile app I'm working on. These easter egg will be triggered when a logo is detected in the camera view. The logo I'm trying to detect is this one: .
I'm not quite sure what the best way to approach this is as I'm pretty new to computer vision. I'm currently finding horizontal edges using the Canny algorithm. I then find line segments using the probabilistic Hough transform. The output of this looks as follows (blue lines represent the line segments detected by the probabilistic Hough transform):
The next step I was going to take would be to look for a group of around 24 lines (fitting within a nearly square rectangle), each line would have to be approximately the same length. I'd use these two signals to indicate the potential presence of the logo. I realise that this is probably a very naive approach and would welcome suggestions as to how to better detect this logo in a more reliable manner?
Thanks
You may want to go with SIFT using Rob Hess' SIFT Library. It's using OpenCV and also pretty fast. I guess that easier than your current way of approaching the logo detection :)
Try also looking for SURF, which claims to be faster & robuster than SIFT. This Feature Detection tutorial will help you.
You may just want to use LogoGrab's technology. It's the best out there and offers all sorts of APIs (both mobile and HTTP). http://www.logograb.com/technologyteam/
I'm not quite sure if you would find such features in the logo to go with a SIFT/SURF approach. As an alternative you can try training a Haar-like feature classifier and use it for detecting the logo, just like opencv does for face detection.
You could also try the Tensorflow's object detection API here:
https://github.com/tensorflow/models/tree/master/research/object_detection
The good thing about this API is that it contains State-of-the-art models in Object Detection & Classification. These models that tensorflow provide are free to train and some of them promise quite astonishing results. I have already trained a model for the company I am working on, that does quite amazing job in LOGO detection from Images & Video Streams. You can check more about my work here:
https://github.com/kochlisGit/LogoLens
I've built an imaging system with a webcam and feature matching such that as I move the camera around; I can track the camera's motion. I am doing something similar to here, except with the webcam frames as the input.
It works really well for "good" images, but when taking images in really low light lots of noise appears (camera high gain), and that messes with the feature detection and matching. Basically, it doesn't detect any good features, and when it does, it cannot match them correctly between frames.
Does anyone know a good solution for this? What other methods are used for finding and matching features?
Here are two example images with very low features:
I think phase correlation is going to be your best bet here. It is designed to tell you the phase shift (i.e., translation) between two images. It is much more resilient (but not immune) to noise than feature detection because it operates in frequency space; whereas, feature detectors operate spatially. Another benefit is, it is very fast when compared with feature detection methods. I have an implementation available in the OpenCV trunk that is sub-pixel accurate located here.
However, your images are pretty much "featureless" with the exception of the crease in the middle, so even phase correlation may have some trouble with it. Think of it like trying to detect translation in a snow storm. If all you can see is white, you can't tell that you have translated at all, thus the term whiteout. In your case, the algorithm might suffer from "greenout" :)
Can you adjust the camera settings to work better in low-light conditions. Have you fully opened the iris? Can you live with lower framerates? Setting a longer exposure time will allow the camera to gather more light, thus giving you more features at the cost of adding motion blur. Or, if low-light is your default environment you probably want something designed for this like an IR camera, but those can be expensive. Other than that, a big lens and long exposures are your friend :)
Histogram equalization may be of interest in improving the image contrast. But, sometimes it can just enhance the noise. OpenCV has a global histogram equalization function called equalizeHist. For a more localized implementation, you'll want to look at Contrast Limited Adaptive Histogram Equalization or CLAHE for short. Here is a good article on it. This page has some nice examples, and some code.