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    <p>Performance is always an issue on mobiles :)</p> <p>There are a few things you can do. <a href="https://stackoverflow.com/questions/11376368/opencv-c-and-c-performance-comparison/11376867#11376867">OpenCV: C++ and C performance comparison</a> explains generic methods on processing time improvements. </p> <p>And some specifics for your project:</p> <ul> <li>If you capture color images and the convert them to grayscale, that is a biig waste of resources. The native camera format is YUV. It gets converted to RGB, which is costly, then to gray, which again is costly. All this while the first channel in YUV (Y) is the grayscale... So, capture YUV, and extract the first channel by copying the first part of the image data (YUV on Android is planar, that means that the first w*h pixels belong to the Y channel)</li> <li>ORB was created to be fast. And it is. But just a few weeks ago FREAK was added to OpenCV. That is a new descriptor, whose authors claim is both more accurate and faster than ORB/SIFT/SURF/etc. Give it a try.YOu can find it in opencv >= 2.4.2 (This is the current now)</li> </ul> <p><strong>EDIT</strong></p> <p>Brad Larsen question is illuminating - if the matcher stays 900ms to process, then that's a problem! Check this post by Andrey Kamaev <a href="https://stackoverflow.com/questions/7232651/how-does-opencv-orb-feature-detector-work/7233103#7233103">How Does OpenCV ORB Feature Detector Work?</a> where he explains the possible combinations between descriptors and matchers. Try the FLANN-based uchar matcher. </p> <p>And also, I suppose you get an awful lot of detections - hundreds or thousands - if it takes that much to match them. Try to limit the detections, or select only the first n best values.</p>
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    1. POKnown object detection performance improvement with OpenCV
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    1. COThank you for your answer, the YUV trick is a great one (would it be faster than using ARM NEON directly?). I'm looking into FREAK right now. Do you have any idea on how to optimize the matching phase?
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    2. COtaking gray from yuv is just a memcpy, so it is faster than anything else. And while I did not check the FREAK source code, they say it can be sped up by SIMD (NEON). So, check the OpenCV code. Without profiling data, I cannot give you but general advices.
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    3. COThat OpenCV integration of FREAK was fast, I was just reading their conference prepublication paper on this a month ago. I'm also experimenting with a GPU-accelerated BRISK-style feature extraction and matching, and had been thinking about modifying FREAK for use on the GPU as well. You're right about just grabbing the Y plane, which is pretty easy on iOS using a YUV planar format for the camera and something like `CVPixelBufferGetBaseAddressOfPlane(cameraFrame, 0)`.
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