Fast SVM Approximations for Object Detection

author: Wolf Kienzle, Max Planck Institute for Biological Cybernetics, Max Planck Institute
published: Feb. 25, 2007,   recorded: May 2004,   views: 645
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

state-of-the-art accuracies in object detection. However, for real time applications, standard SVMs are usually too slow. In this work, we propose a method for approximating an SVM detector in terms of a small number of separable nonlinear filters. We are building on work of Romdhani et al. (ICCV 2001), where an SVM face detector was approximated using the so-called reduced set algorithm and evaluated in a cascade. However, when using plain gray values as features, we found it more effective to reduce the high computational cost for the pixel-wise comparisons, rather than focusing on sparsity of the detectors alone. In our approach, we constrain the reduced set optimization to a class of nonlinear convolution filters which can be evaluated more efficiently (i.e. O(w+h) instead of O(wh), where w and h are the patch dimensions, respectively). We demonstrate a prototype of our system which runs in real time on a standard PC.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: