Results of objection detection using SURF+homography
Hi I was trying to detect an object in a video feed. So far I have explored Template Matching but the results wasn't satisfactory as the video feed may or may not contain the object and is subjected to rotation and differing scale. Furthermore I have no time to train a cascade classifier or sorts, therefore I have stumbled upon SURF+homography approach from this link: http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography.html.
The results was good but what I wanted to "get" from my program is simply an answer to "is the object currently present in the video?" (Present/Absent). So how can I get this answer using the SURF+homography approach?
Could you explain why training a cascade isn't possible? I mean, a basic cascade takes only an hour or so to train of you use LBP features and it could already provide some good results.
@StevenPuttemans Oh I didn't realize it would take such short time as from what I read it requires hours, even days to train a classifier. However would you mind pointing me a direction to, perhaps a tutorial on training a LBP cascade in a short time? Thanks a lot.
That is if your data contains sets of 5000 pos 5000 negatives and you use for example HAAR wavelets. Just start by using 500 pos and 500 negatives, don't set your required precision to high and it will work quite fast. I always use LBP for prototyping.
As for examples, just search the forum, it is filled with topics concernings this cascade classifier training. The official one is found here. Just select the correct features!
In fact this is one of the reasons I chose not to use a classifier, the efforts in locating "good" positive samples and negative samples. Anyway thanks for the suggestion, I'll try it if I have more time.
Yeah it is indeed the bottleneck of the actual algorithms using these positive and negative training sets...