Learning SURF Cascade for Fast and Accurate Object Detection

Jianguo Li, Yimin Zhang

Abstract:

This paper presents a novel learning framework for
training boosting cascade based object detector from large
scale dataset. The framework is derived from the well known Viola-Jones (VJ) framework but distinguished by
three key differences. First, the proposed framework adopts
multi-dimensional SURF features instead of single dimensional Haar features to describe local patches. In this way,
the number of used local patches can be reduced from hundreds of thousands to several hundreds. Second, it adopts
logistic regression as weak classifier for each local patch
instead of decision trees in the VJ framework. Third, we
adopt AUC as a single criterion for the convergence test
during cascade training rather than the two trade-off criteria (false-positive-rate and hit-rate) in the VJ framework.
The benefit is that the false-positive-rate can be adaptive
among different cascade stages, and thus yields much faster
convergence speed of SURF cascade.
Combining these points together, the proposed approach
has three good properties. First, the boosting cascade can
be trained very efficiently. Experiments show that the proposed approach can train object detectors from billions of
negative samples within one hour even on personal computers. Second, the built detector is comparable to the state of-the-art algorithm not only on the accuracy but also on
the processing speed. Third, the built detector is small in
model-size due to short cascade stages.