Object detection via fusion of global classifier and part-based classifier

Authors:
Zhi Zeng;Shengjin Wang;Xiaoqing Ding
Affiliations:
Department of Electronic Engineering, Tsinghua University, Beiing, China;Department of Electronic Engineering, Tsinghua University, Beiing, China;Department of Electronic Engineering, Tsinghua University, Beiing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

We present a framework for object detection via fusion of global classifier and part-based classifier in this paper. The global classifier is built using a boosting cascade to eliminate most non-objects in the image and give a probabilistic confidence for the final fusion. In constructing the part-based classifier, we boost several neural networks to select the most effective object parts and combine the weak classifiers effectively. The fusion of these two classifiers generates a more powerful detector either on efficiency or accuracy. Our approach is evaluated on a database of real-world images containing rear-view cars. The fused classifier gives distinctively superior performance than traditional cascade classifiers.