When detecting objects using SURF, how can a plot a graph for false positives and hits using the Good matches and several keypoints?
(A) How do I get the statistics of good matches i.e an ROC plot or the true positives vs false positives of detection from so many of the line descriptors?Can somebody put a code for plotting true positves vs false positive statistics.
(B)**Secondly,there are many resources vdo1 , vdo2and implemetations, papers ( Object tracking using improved Camshift with SURF method ;
A Study on Moving Object Tracking Algorithm Using SURF Algorithm
and Depth Information
) which say that SURF and SIFT can be used for tracking in combination with camshift or meanshift.
But, what I fail to understand is that we need prediction algorithm like Kalman filters or tracking algorithm like Camshift,mean shift or template differencing(not sure) for tracking.So,how come some video implementations and tutorial say that Lukas Kanade Optical flow,SIFT,SURF is tracking objects whereas the papers mention to club either camshift or meanshift.Am I missing out on some conceptual matter?
Shall be obliged for pointers and a detailed explanation on how SURF or SIFT or feature based methods can be used for tracking alone?
Lucas-Kandae with pyramid (pyrLK) is a method that looks for a small shift in a single feature location. It can do this to many features at once. Camshift and meanshift track a statistic for a group of features. You can also just try to use a matcher, to find where the features went on the next frame. GoodFeturesToTrack, SIFT and SURF are algorithms that find points that should be easy to find and tell apart one from another. SURF and SIFT include also descriptors, that characterise those features in a way which can ignore size change, orientation change or both.
Kalman filter is used to refine Your results. It is able to shrink the area where the answer should lay, because algorithms above are not perfect.
As for the code, I haven't done too much tracking except Shi-Thomasi + pyrLK, so I dont't think I can help.
Related
I have extracted DenseSIFT from the query and database image and quantized by kmeans using VLFeat. The challenge is to find those SIFT features that quantized to the same visual words and be spatially consistent (have a similar position to object centers). I have tried few techniques:
using FLANN() on the SIFT (normal SIFT) coordinates on both query and database image and find the nearest neighbor and then comparing the visual words (NOTE: this gave few points that did not work).
Using Coherent-Point-Drift (CPD) on SIFT coordinates to find the matched points (I am not sure about this whether it is a right solution or not).
I am struggling with it for many days, and I hope experts can guide me with this. What are the possible solutions or algorithms that I can use for solving this?
Neither of those two methods you mentioned achieve what you want do. The answer depends on the object in your pictures. If it has mostly flat faces, then you can rely on estimating the homography, see this tutorial.
If that's not case then can use the epipolar constraint to remove outliers / get geometrically consistent matches, see this tutorial. There are some other ways to achieve this if the speed is of importance in your application.
I've been playing with the excellent GPUImage library, which implements several feature detectors: Harris, FAST, ShiTomas, Noble. However none of those implementations help with the feature extraction and matching part. They simply output a set of detected corner points.
My understanding (which is shakey) is that the next step would be to examine each of those detected corner points and extract the feature from then, which would result in descriptor - ie, a 32 or 64 bit number that could be used to index the point near to other, similar points.
From reading Chapter 4.1 of [Computer Vision Algorithms and Applications, Szeliski], I understand that using a BestBin approach would help to efficient find neighbouring feautures to match, etc. However, I don't actually know how to do this and I'm looking for some example code that does this.
I've found this project [https://github.com/Moodstocks/sift-gpu-iphone] which claims to implement as much as possible of the feature extraction in the GPU. I've also seen some discussion that indicates it might generate buggy descriptors.
And in any case, that code doesn't go on to show how the extracted features would be best matched against another image.
My use case if trying to find objects in an image.
Does anyone have any code that does this, or at least a good implementation that shows how the extracted features are matched? I'm hoping not to have to rewrite the whole set of algorithms.
thanks,
Rob.
First, you need to be careful with SIFT implementations, because the SIFT algorithm is patented and the owners of those patents require license fees for its use. I've intentionally avoided using that algorithm for anything as a result.
Finding good feature detection and extraction methods that also work well on a GPU is a little tricky. The Harris, Shi-Tomasi, and Noble corner detectors in GPUImage are all derivatives of the same base operation, and probably aren't the fastest way to identify features.
As you can tell, my FAST corner detector isn't operational yet. The idea there is to use a lookup texture based on a local binary pattern (why I built that filter first to test the concept), and to have that return whether it's a corner point or not. That should be much faster than the Harris, etc. corner detectors. I also need to finish my histogram pyramid point extractor so that feature extraction isn't done in an extremely slow loop on the GPU.
The use of a lookup texture for a FAST corner detector is inspired by this paper by Jaco Cronje on a technique they refer to as BFROST. In addition to using the quick, texture-based lookup for feature detection, the paper proposes using the binary pattern as a quick descriptor for the feature. There's a little more to it than that, but in general that's what they propose.
Feature matching is done by Hamming distance, but while there are quick CPU-side and CUDA instructions for calculating that, OpenGL ES doesn't have one. A different approach might be required there. Similarly, I don't have a good solution for finding a best match between groups of features beyond something CPU-side, but I haven't thought that far yet.
It is a primary goal of mine to have this in the framework (it's one of the reasons I built it), but I haven't had the time to work on this lately. The above are at least my thoughts on how I would approach this, but I warn you that this will not be easy to implement.
For object recognition / these days (as of a couple weeks ago) best to use tensorflow /Convolutional Neural Networks for this.
Apple has some metal sample code recently added. https://developer.apple.com/library/content/samplecode/MetalImageRecognition/Introduction/Intro.html#//apple_ref/doc/uid/TP40017385
To do feature detection within an image - I draw your attention to an out of the box - KAZE/AKAZE algorithm with opencv.
http://www.robesafe.com/personal/pablo.alcantarilla/kaze.html
For ios, I glued the Akaze class together with another stitching sample to illustrate.
detector = cv::AKAZE::create();
detector->detect(mat, keypoints); // this will find the keypoints
cv::drawKeypoints(mat, keypoints, mat);
// this is the pseudo SIFT descriptor
.. [255] = {
pt = (x = 645.707153, y = 56.4605064)
size = 4.80000019
angle = 0
response = 0.00223364262
octave = 0
class_id = 0 }
https://github.com/johndpope/OpenCVSwiftStitch
Here is a GPU accelerated SIFT feature extractor:
https://github.com/lukevanin/SIFTMetal
The code is written in Swift 5 and uses Metal compute shaders for most operations (scaling, gaussian blur, key point detection and interpolation, feature extraction). The implementation is largely based on the paper and code from the "Anatomy of the SIFT Method Article" published in the Image Processing Online Journal (IPOL) in 2014 (http://www.ipol.im/pub/art/2014/82/). Some parts are based on code by Rob Whess (https://github.com/robwhess/opensift), which I believe is now used in OpenCV.
For feature matching I tried using a kd-tree with the best-bin first (BBF) method proposed by David Lowe. While BBF does provide some benefit up to about 10 dimensions, with a higher number of dimensions such as used by SIFT, it is no better than quadratic search due to the "curse of dimensionality". That is to say, if you compare 1000 descriptors against 1000 other descriptors, it stills end up making 1,000 x 1,000 = 1,000,000 comparisons - the same as doing brute-force pairwise.
In the linked code I use a different approach optimised for performance over accuracy. I use a trie to locate the general vicinity for potential neighbours, then search a fixed number of sibling leaf nodes for the nearest neighbours. In practice this matches about 50% of the descriptors, but only makes 1000 * 20 = 20,000 comparisons - about 50x faster and scales linearly instead of quadratically.
I am still testing and refining the code. Hopefully it helps someone.
I wonder how do we evaluate feature detection/extraction methods (SIFT,SURF,MSER...) for object detection and tracking like pedestrians, lane vehicles etc.. Are there standard metrics for comparison? I have read blogs like http://computer-vision-talks.com/2011/07/comparison-of-the-opencvs-feature-detection-algorithms-ii/ some research papers like this. The problem is the more I learn the more I am confused.
It is very hard to estimate feature detectors per se, because features are only computation artifacts and not things that you are actually searching in images. Feature detectors do not make sense outside their intended context, which is affine-invariant image part matching for the descriptors that you have mentioned.
The very first usage of SIFT, SURF, MSER was multi-view reconstruction and automatic 3D reconstruction pipe-lines. Thus, these features are usually assessed from the quality of the 3D reconstrucution or image part matching that they provide. Roughly speaking, you have a pair of images that are related by a known transform (an affinity or an homography) and you measure the difference between the estimated homography (from the feature detector) and the real one.
This is also the method used in the blog post that you quote by the way.
In order to assess the practical interest of a detector (and not only its precision in an ideal multi-view pipe-line) some additional measurements of stability (under geometric and photometric changes) were added: does the number of detected features vary, does the quality of the estimated homography vary, etc.
Accidentally, it happens that these detectors may also work (also it was not their design purpose) for object detection and track (in tracking-by-detection cases). In this case, their performance is classically evaluated from more-or-less standardized image datasets, and typically expressed in terms of precision (probability of good answer, linked to the false alarm rate) and recall (probability of finding an object when it is present). You can read for example Wikipedia on this topic.
Addendum: What exactly do I mean by accidentally?
Well, as written above, SIFT and the like were designed to match planar and textured image parts. This is why you always see example with similar images from a dataset of graffiti.
Their extension to detection and tracking was then developed in two different ways:
While doing multiview matching (with a spherical rig), Furukawa and Ponce built some kind of 3D locally-planar object model, that they applied then to object detection in presence of severe occlusions. This worlk exploits the fact that an interesting object is often locally planar and textured;
Other people developed a less original (but still efficient in good conditions) approach by considering that they had a query image of the object to track. Individual frame detections are then performed by matching (using SIFT, etc.) the template image with the current frame. This exploits the fact that there are few false matchings with SIFT, that objects are usually observed in a distance (hence are usually almost planar in images) and that they are textured. See for example this paper.
I'm trying to evaluate Feature Detectors and Descriptors with the FLANN algorithm based on this tutorial
I want to build a ROC curve for the evaluation part therefore I have to get the TP, FN, FP and TN. The thing is, I don't know how to get these values! I have read a lot of papers but none of them explain, for instance how they get the false positives. In the given tutorial you can set a certain threshold such that you can count the good and the bad matches, but thats not a justification that everthing was matched correctly. Should I count it for every image pairs by hand or is their a common technique do solve it automatically.
Thanks in advance for any help!
You have to have so called "ground truth" - manually checked correspondences or transformation matrix (fundamental or homography) between two images. Correspondences which are consistent with this matrix are correct.
Check approach used in classical papers by Mykolajczyk et al. "A comparison of affine region detectors", "A PERFORMANCE EVALUATION OF LOCAL DESCRIPTORS" and Moreels and Perona "Evaluation of Features Detectors and Descriptors based on 3D Objects"
I would like to know, if there is any code or any good documentation available for implementing HOG features? I tried to read the documentation here but it's quite difficult to understand and it needs SVM..
What I need is just to implement a HOG detector for objects.... Like what it does SIFT or SURF
Btw, I'm not interesting in this work.
Thank you..
you can take a look at
http://szproxy.blogspot.com/2010/12/testtest.html
he also published "tutorial" for HOG on source forge here:
http://sourceforge.net/projects/hogtrainingtuto/?_test=beta
I know this since I'm having the same problem as you. The tutorial though isn't what i would call a tutorial, its a bunch of source codes, no documentation, but I assume that it works and can at least get you somewhere.
At the end and simplifying a bit, all that you need to detect specific objects in image is:
Localize "points of interest" to extract the patches:
In order to get points of interest, you can use some algorithms like Harris corner detector, randomly or something simply like sliding windows.
From these points get patches:
You will have to take the decission of the patch size.
From these patches compute the feature descriptor. (like HOG).
Instead of HOG you can use another feature descriptor like SIFT, SURF...
HOG's implementation is not too hard. You have to calculate the gradients of the extracted patch doing applying Sobel X and Y kernels, after that you have to divide the patch in NxM cells, 8x8 for instance, and compute an histogram of gradients, angle and magnitude. In the following link you can see it more detailed explanation:
HOG Person Detector Tutorial
Check your feature vector in the previously trained classifier
Once you got this vector, check if it is the desired object or not with a previously trained classifier like SMV. Instead SVM you could use NeuralNetworks for instance.
SVM implementation is more dificult, but there are some libraries like opencv that you can use.
There is a function extractHOGFeatures in the Computer Vision System Toolbox for MATLAB.