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Hi,

I have started using opencv from past few weeks. I am learning about ways of doing object detection. I see that there are primarily 2 different approaches to do object detection - 1. use positive and negative image set, build classifier and use the classifier to detect objects 2. use the keypoint detection, descriptor extraction and matching to detect objects

I would like to know when to apply which technique? I also found that the 2nd method internally has multiple algorithms to do each step like SIFT, SURF, ORB, FlannMatcher so on. My primary aim is to first understand when to use classifier technique and when to use feature extraction technique? Once I understand that, I can further look at the different approaches within them.

Please correct me if my understanding above is wrong and let me know the differences between the 2 approaches.

Hi,

I have started using opencv from past few weeks. I am learning about ways of doing object detection. I see that there are primarily 2 different approaches to do object detection - 1. use detection:

1. Use positive and negative image set, build classifier and use the classifier to detect objects 2. use objects
2. Use the keypoint detection, descriptor extraction and matching to detect objects

I would like to know when to apply which technique? I also found that the 2nd method internally has multiple algorithms to do each step like SIFT, SURF, ORB, FlannMatcher so on. My primary aim is to first understand when to use classifier technique and when to use feature extraction technique? Once I understand that, I can further look at the different approaches within them.

Please correct me if my understanding above is wrong and let me know the differences between the 2 approaches.