I'm trying to train a cascade to detect the character '1' on a blank piece of paper. So far I've been using subsets of the 3019 background images from Naotoshi Seo's tutorial. Just wondering if anyone knows a good way to train something against a known background? opencv_traincascade doesn't seem to like me using one image as the negative sample. I'm only using one positive sample before running opencv_createsamples. How should I set the rotations during opencv_createsamples?
Also, just to clarify, I'm using LBP training rather than haartraining.
Micka's comment is sensible in that a "known background" is never constant.
The best background negative images are realistic ones, eg texture constrasts, shadings, lighting in general, whatever will be the real background.
Related
I am new to AI/ML and am trying to use the same for solving the following problem.
I have a set of (custom) images which while having common characteristics also will have a unique pattern/signature and color value. What set of algorithms should I use to have the pass in following manner:
1. Recognize the common characteristic (like presence of a triangle at any position in a 10x10mm image). If present, proceed, else exit.
2. Identify the unique pattern/signature to identify each image individually. The pattern/signature could be shape (visible to human eye or hidden like using an overlay shape using background image with no boundaries).
3. Store color tone/hue/saturation to determine any loss/difference (maybe because the capture source is different from the original one).
While this is in way similar to face recognition algo, for me saturation/shadow will matter while being direction independent.
I figure that using CNN may be the way to go for step#2 and SVN for step#1, any input on training, specifics will be appreciated. What about step#3, use BGR2HSV? The objective is to use ML/AI and not get into machine-vision.
Recognize the common characteristic (like presence of a triangle at any position in a 10x10mm image). If present, proceed, else exit.
In a sense, what you want is a classifier that can detect patterns in an image. However, we can train classifiers to detect certain types of patterns in images.
For example, I can train a classifier to recognise squares and circles, but if I show it an image with a triangle in it, I cannot expect it to tell me it is a triangle, because it has never seen it before. The downside is, your classifier will end up misclassifying it as one of the shapes it knows to exist: either square or circle. The upside is, you can prevent this.
Identify the unique pattern/signature to identify each image individually.
What you want to do is train a classifier on a large amount of labelled data. If you want the classifier to detect squares, circles, or triangles in an image, you must train it with a large amount of labelled images of squares, circles and triangles.
Store color tone/hue/saturation to determine any loss/difference (maybe because the capture source is different from the original one).
Now, you are leaving the territory of simple image labelling and entering the world of computer vision. This is not as simple as a vanilla image classifier, but it is possible and there are a lot of online tools to help you do this. For example, you may take a look at OpenCV. They have an implementation in python and C++.
I figure that using CNN may be the way to go for step#2 and SVN for
step#1
You can combine step 1 and step 2 with a Convolutional Neural Network (CNN). You do not need to use a two step prediction process. However, beware, if you pass the CNN an image of a car, it will still label it as a shape. You can, again circumvent this by training it on a million positive samples of shapes, and a million negative samples of random other images with the class "Other". This way, anything that is not a shape will get classified into "Other". This is one possibility.
What about step#3, use BGR2HSV? The objective is to use ML/AI and not
get into machine-vision.
With the inclusion of this step, there is no option but to get into computer vision. I am not exactly sure how to go about this, but I can guarantee OpenCV will provide you a way to do this. In fact, with OpenCV, you will no longer need to implement your own CNN, because OpenCV has its own image labelling libraries.
Im trying implement a real time object classification program using SVM classification and BoW clustering algorithms. My questions is what are the good practices for selecting positive and negative training images?
Positive image sets
Should the background be empty? Meaning, should the image only contain the object of interest? When implementing this algorithm in real time, the test image will not contain only the object of interest, it will definitely have some information from the background as well. So instead of using isolated image collection, should I choose images which look more similar to the test images?
Negative image sets
Can these be any image set without the object of interest? Or should they be from the environment where this algorithm is going to be tested without object of interest?. For example, if I'm going to classify phones in my living room environment, should negatives be the background image set of my living room environment without the phone in the foreground? or can it be any image set? (like kitchen, living room, bedroom or outdoor images) Im asking this because, I don't want the system to be environment-specific. Must be robust at any environment (indoors and outdoors)
Thank you. Any help or advice is much appreciated.
Positive image sets
Yes you should definitely choose images which look more similar to the test images.
Negative image sets
It can be any image set however, it is better to include images from the environment where this algorithm is going to be tested without object of interest.
Generally
Please read my answer to some other SO question, it would be useful. Discussion continued in comments, so that might be useful as well.
I'm making a program to detect shapes from an r/c plane for a competition. I have no real images of the targets, but I do have computer generated examples of them on the rules.
My question is, can I train my program to detect real world objects based on computer generated shapes or should I find a different method to complete this task?
I would like to know before I foolishly generate 5k samples and find them useless in the end.
EDIT: I also don't know the exact color of the objects. If I feed the program samples of varying color, will it be a problem?
Thanks in advance!!
Edit2: Here's what groups from my school detected in previous years
As you can see, the detected images are not nearly as flawless as what would appear in real life. If you can suggest a better method, that would help.
If you think that the real images will have unique colors with simple geometric shapes then you could probably try to create a normalized Hue-histogram. Use it to train SVM classifier. The benefit of using Hue-histogram is that it will be rotational and scale invariant.
You can take the few precautions in mind:
Don't forget to remove the illumination affects.
Sometimes, White and black pixels create some problem in hue-histogram calculation so try to remove them from calculation by considering only those pixel which have S>0 and V>0 in S & V channels of HSV image.
I would rather suggest you to use the real world images because the performance is largely dependent upon training (my personal experience). And why don't you try to use SIFT/SURF descriptors for training to SVM (support vector machine) as SIFT/SURF are scale as well as rotational invariant.
Information:
I would like to use OpenCV's HOG detection to identify objects that can be seen in a variety of orientations. The only problem is, I can't seem to find a reasonable feature detector or classifier to detect this in a rotation and scale invaraint way (as is needed by objects such as forearms).
Prior Work:
Lets focus on forearms for this discussion. A forearm can have multiple orientations, the primary distinct features probably being its contour edges. It is possible to have images of forearms that are pointing in any direction in an image, thus the complexity. So far I have done some in depth research on using HOG descriptors to solve this problem, but I am finding that the variety of poses produced by forearms in my positives training set is producing very low detection scores in actual images. I suspect the issue is that the gradients produced by each positive image do not produce very consistent results when saved into the Histogram. I have reviewed many research papers on the topic trying to resolve or improvie this, including the original from Dalal & Triggs [Link]: http://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf It also seems that the assumptions made for detecting whole humans do not necessary apply to detecting individual features (particularly the assumption that all humans are standing up seems to suggest HOG is not a good route for rotation invariant detection like that of forearms).
Note:
If possible, I would like to steer clear of any non-free solutions such as those pertaining to Sift, Surf, or Haar.
Question:
What is a good solution to detecting rotation and scale invariant objects in an image? Particularly for this example, what would be a good solution to detecting all orientations of forearms in an image?
I use hog to detect human heads and shoulders. To train particular part you have to give the location of it. If you use opencv, you can clip samples containing only the training part you want, and make sure all training samples share the same size. For example, I clip images to contain only head and shoulder and resize all them to 64x64. Other opensource codes may require you to pass the location as the input parameter, essentially the same.
Are you trying the Discriminatively trained deformable part model ?http://www.cs.berkeley.edu/~rbg/latent/
you may find answers there.
I'm looking for the fastest and more efficient method of detecting an object in a moving video. Things to note about this video: It is very grainy and low resolution, also both the background and foreground are moving simultaneously.
Note: I'm trying to detect a moving truck on a road in a moving video.
Methods I've tried:
Training a Haar Cascade - I've attempted training the classifiers to identify the object by taking copping multiple images of the desired object. This proved to produce either many false detects or no detects at all (the object desired was never detected). I used about 100 positive images and 4000 negatives.
SIFT and SURF Keypoints - When attempting to use either of these methods which is based on features, I discovered that the object I wanted to detect was too low in resolution, so there were not enough features to match to make an accurate detection. (Object desired was never detected)
Template Matching - This is probably the best method I've tried. It's the most accurate although the most hacky of them all. I can detect the object for one specific video using a template cropped from the video. However, there is no guaranteed accuracy because all that is known is the best match for each frame, no analysis is done on the percentage template matches the frame. Basically, it only works if the object is always in the video, otherwise it will create a false detect.
So those are the big 3 methods I've tried and all have failed. What would work best is something like template matching but with scale and rotation invariance (which led me to try SIFT/SURF), but i have no idea how to modify the template matching function.
Does anyone have any suggestions how to best accomplish this task?
Apply optical flow to the image and then segment it based on flow field. Background flow is very different from "object" flow (which mainly diverges or converges depending on whether it is moving towards or away from you, with some lateral component also).
Here's an oldish project which worked this way:
http://users.fmrib.ox.ac.uk/~steve/asset/index.html
This vehicle detection paper uses a Gabor filter bank for low level detection and then uses the response to create the features space where it trains an SVM classifier.
The technique seems to work well and is at least scale invariant. I am not sure about rotation though.
Not knowing your application, my initial impression is normalized cross-correlation, especially since I remember seeing a purely optical cross-correlator that had vehicle-tracking as the example application. (Tracking a vehicle as it passes using only optical components and an image of the side of the vehicle - I wish I could find the link.) This is similar (if not identical) to "template matching", which you say kind of works, but this won't work if the images are rotated, as you know.
However, there's a related method based on log-polar coordinates that will work regardless of rotation, scale, shear, and translation.
I imagine this would also enable tracking that the object has left the scene of the video, too, since the maximum correlation will decrease.
How low resolution are we talking? Could you also elaborate on the object? Is it a specific color? Does it have a pattern? The answers affect what you should be using.
Also, I might be reading your template matching statement wrong, but it sounds like you are overtraining it (by testing on the same video you extracted the object from??).
A Haar Cascade is going to require significant training data on your part, and will be poor for any adjustments in orientation.
Your best bet might be to combine template matching with an algorithm similar to camshift in opencv (5,7MB PDF), along with a probabilistic model (you'll have to figure this one out) of whether the truck is still in the image.