PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:8330
Deposited By:Sunando Sengupta
Deposited On:20 October 2011