[Paper Review] Rapid Object Detection using a Boosted Cascade of Simple Features

This paper showed how to apply machine learning techniques in the area of high-speed object detection (or more precisely, template matching). The idea is to choose one kind of coarse features that can be computed very efficiently and to improve their performance by AdaBoost learning and filter cascading. The proposed system can run in real-time in face-detection with comparable accuracy to the state-of-the-art. 

 

Apparently, the most important contribution of this work is in extending AdaBoost so that one can choose from a large pool of available features to form a small, yet equivalently discriminative subset. The extension is not complex, however - you only need to repeat the training in each iteration for all available classifiers (features) instead and select the most promising one. The other two ideas of this work are more straightforward. The integral image is a synonym of one old trick in dynamic programming, and filter cascading is something everyone would possibly do in high-speed application. Nevertheless, the resulting system does demonstrate a great improvement over existing methods (in face detection).

 

One pretty vital problem that was ignored in the paper is the object orientation. The proposed framework seems not to be possible to deal with rotated objects with simple modification. Although one may blindly repeat the same detection algorithm at every possible angle, this would severely slow down the system. I think this should be the cause that I didn't see anyone apply the same idea to objects other than the frontal face.