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I have been working around with OpenCV for few days now and I have a project where I should detect cars and humans from the sky.
So here are my inputs:
And here are my output:
Based on that, my question is as follows: Which one between Haar Cascade and Hog Detection would you recommend to do so and why? Or any else?
Many thanks for your answers
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You need to change scale factor and minimum neighbours in HAAR cascade which is not same for all the image. So it's better to use HOG.
An LBP cascade can be trained to perform similarly (or better) than the Haar cascade, but out of the box, the Haar cascade is about 3x slower, and depending on your data, about 1-2% better at accurately detecting the location of a face.
HOG is a simple and powerful feature descriptor. It is not only used for face detection but also it is widely used for object detection like cars, pets, and fruits. HOG is robust for object detection because object shape is characterized using the local intensity gradient distribution and edge direction.
Some Haar cascade benefits are that they're very fast at computing Haar-like features due to the use of integral images (also called summed area tables). They are also very efficient for feature selection through the use of the AdaBoost algorithm.
HOG is usually better for human detection, than Haar. I have only experience in this so I thought I'd give some input on that. However, the limitation of HOG is that the human must be within a "perfect" area on the screen. Too close, it won't detect the human. Too far, it won't detect the human.
I have had better luck with HOG than Haar. Haar gave me too many false positives.
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