Proposal Generation for Object Detection using Cascaded Ranking SVMs
Ziming Zhang, Jonathan Warrell and Philip Torr
In: CVPR 2011, June 21-23, 2011, Colorado Springs.
Object recognition has made great strides recently.
However, the best methods, such as those based on kernel-
SVMs are highly computationally intensive. The problem of
how to accelerate the evaluation process without decreasing
accuracy is thus of current interest. In this paper, we
deal with this problem by using the idea of ranking. We
propose a cascaded architecture which using the ranking
SVM generates an ordered set of proposals for windows
containing object instances. The top ranking windows may
then be fed to a more complex detector. Our experiments
demonstrate that our approach is robust, achieving higher
overlap-recall values using fewer output proposals than the
state-of-the-art. Our use of simple gradient features and
linear convolution indicates that our method is also faster
than the state-of-the-art.