Hi!
I'm currently implementing a system for multi-class detection in an underwater environment. I have chosen to use the LBP-HF features found here to deal with rotation and illumination invariance. For the actual detection I have trained classifiers using libSVM, and I have successfully detected objects in my input images (1920 x 1080) by the use of a sliding window to achieve location invariance and a image scale pyramid for scale invariance. The problem with this approach is that it is very slow on my large input images, so I am trying to find ways of speeding it up. I have read a bit about efficient subwindow search (ESS), but to me it seems that this technique requires confidence scores for each prediction to be used. Is this true? Are there any other ways I could speed up my detection scheme? Any help would be appreciated!
PS: The reason why this is posted here is because the code is written for a framework using OpenCV.