I want to extract HOG features using the OpenCV Module. I just want the features, not the whole svm detection process.
To speed up things, I would like to use the HOGCache object.
My question: is this possible to use the HOGCache on a specified zone of the image, and then call the HOG compute method (or a changed version, I guess) to extract the HOG vector at a specified location ?
The HOG compute has to be called at several locations, and not using the cache gives me a really slow process.
I guess this is possible since the svm detector use the cache, but with a a sliding window.
Thx for your help
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I want to build my own Object Detection algorithm using HOG feature. Since OpenCV has its own framework to do this for pedestrian detect, I think I can just modify some of the parameters to customize my own one. But I have a few questions about it after reading this.
1.Prepare my own dataset:
Do I have to make all the pos and neg images the same size? Sometimes resize the image may lead to image deformation and affect the hog result. If not, then I have to change the HOG parameters to adapt every image(for example: set the window size = image size and generate a 3780 vector). which one is better.(I prefer the second one)
2.For training the SVM.
In opencv, I think they use SVMLight for training which has been integrated in OpenCV. Can I use Libsvm or other package (compatible with function hog.setSVMDetector())?
3.For hog.detectMultiScale() function
After doing all these above, can I get the result (rectangles) just calling this function?
Thanks!
I am trying to develop an automatic(or semi-automatic) image annotator for my final year project with OpenCV. I have been studying many OpenCV resources and have come across cascade classification for training and detection purposes. I understood that part, and also tried the Face Detection tutorial provided with OpenCV. So, now I know how to train and detect objects.
However, I still cannot understand how can I annotate objects present in the image?
For example, the system will show that this is an object, but I want the system to show that it is a ball. How can i accomplish that?
Thanks in advance.
One binary classificator (detector) can separate objects by two classes:
positive - the object type classifier was trained for,
and negative - all others.
If you need detect several distinguished classes you should use one detector for each class, or you can train multiclass classifier ("one vs all" type of classifiers for example), but it usually works slower and with less accuracy (because detector better search for similar objects). You can also take a look at convolutional networks (by Yann LeCun).
This is a very hard task. I suggest simplifying it by using latent SVM detector and limiting yourself to the models it supplies:
http://docs.opencv.org/modules/objdetect/doc/latent_svm.html
I'm using OpenCV library (C++) to extract detectors from 2 images coming from a video stream taker from an aerial camera in order to, afterwards, find the matching points in successive images. i'm wondering which is the best algorithm to find robust detectors of a urban environment??
Ps. Actually I'm using SURF but when the images changes a little (because the camera is translating very slowly) the matchings between these descriptors become very few!
If you want to try different aproaches give a try to RoboRealm , they have a trial version, you just put the algoritms and seems the results, for testing purposes even if you will use OpenCV its ok.
I'm trying to build a lightweight object recognition system using ORB for feature extraction and LDA for classification. But I'm running into an issue do to the varying size of extracted features.
These are my steps:
Extract keypoints using ORB.
Extract trainable features in the image by grouping the keypoints.
(example of whats being extracted: http://imgur.com/gaQWk)
Train the recognizer with the extracted features. (This is where problems arise)
Classify objects in an image from the wild.
If I attempt to create a generalized matrix using cv::gemm, I get an exception due to the varying sizes. My first thought was to just to normalize all the images by resizing them, but this causes a lot of accuracy issues when objects have similar small features.
Is there any solution to this? Is LDA an appropriate method for this? I know it's commonly used with facial recognition algorithms such as fisherfaces.
LDA requires fixed length features, as do most optimization and machine learning methods. You could resize the image patches to be a fixed size, but that is probably not going to be a good feature. Normally people use a scale invariant feature such as SIFT. You also might try a color histogram, or some variation of edge detection and spatial histogram binning such as a GIST vector.
It's hard to say if LDA is an appropriate method for this without knowing what you hope to accomplish. You might also look into using SVM, some form of boosting, or just plain nearest neighbor with a large training set.
I have a simple question, which I want to know, what kind of libraries are available and can give good results for implementing SIFT, HOG(Histogram Oriented Gradient) and SURF in c++ or opencv?
Hence: 1- Give me the link for the code if you can, which I will be so appreciated.
2- If you know one of them or any kind of information to lead me to what I want, I will be so appreciated as well.
Thanks
check these:
surf
- great article
http://people.csail.mit.edu/kapu/papers/mar_mir08.pdf
sift
- great source, I tried it on the iPhone
http://blogs.oregonstate.edu/hess/
- fast - fast corner detection library
http://svr-www.eng.cam.ac.uk/~er258/work/fast.html
Example of surf code in openCV
https://code.ros.org/trac/opencv/browser/trunk/opencv/samples/cpp/matching_to_many_images.cpp
Not sure if this is still relevant, but you also get two implementations of computing HOG descriptors in opencv i.e. both GPU and CPU versions of the HOG code.
for the CPU version you can check this blog post
however in the CPU version you would need to write your own logic for sliding windows.
and the GPU version is fairly straightforward you can read the documentation here
Might help you to know that SIFT and SURF implementations are already integrated into OpenCV.
http://opencv.willowgarage.com/documentation/cpp/features2d__feature_detection_and_descriptor_extraction.html
Be careful about OpenCV implementations, because latest versions of OpenCV have classified SIFT and SURF implementations as nonfree http://docs.opencv.org/modules/nonfree/doc/nonfree.html.
Now you can use them, but probably they are subject to licensing and cannot be used for commercial solutions.
This one uses descriptors based on HoG, Sobel and Lab channels for detection Class-Specific Hough Forests for Object Detection (opencv/c source code).
Rather then performing detection at every possible location this approach calculates a vote for each descriptor, then when putted together they produce a voting cloud where maximum will correspond to most probable location of the target. When combined with cvGoodFeaturesToTrack can produce very good results, even with a small training database.