Is Haar Cascade the only available technique for image recognition in OpenCV

Question

I know that there are many detection techniques in OpenCV, such as SURF, STAR, ORB etc...but those techniques are for feature detection of new video feed, not for dealing with specific instances of objects that require prior learning. OpenCV's documentation isn't quite as easy to flip through and I've yet been able to find anything besides Haar, which I know deals best with face recognition.

So are there any other techniques besides Haar? The Haar technique dates back to research 10 years ago, so ideally I hope that there have been some more advances since then that have been implemented in OpenCV.

Answer 1

If you are looking for OpenCV machine learning type algorithms, check out this link.

For a state of the art on-the-fly object detection algorithm, have a look at OpenTLD. It uses bounding boxes and random forests to learn about an object over time. Check out the demo video here.

Also check out the matching_to_many_images.cpp sample from OpenCV. It uses feature descriptors to match objects much like Google Goggles works. A related example to this is the bagofwords_classification.cpp sample. It may be what you are looking for in this case. It uses feature detectors (SURF, SIFT, etc...) to detect objects and then classify them by comparing the relative positions of the features to a learned database of features. Have a look also at this tutorial from MIT.

The latentsvmdetect.cpp may also be a good starting point for you.

Hope that helps!