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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zeng, Z., Wang, S., Ding, X. (2006). Object Detection Via Fusion of Global Classifier and Part-Based Classifier. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_62
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DOI: https://doi.org/10.1007/11760023_62
Publisher Name: Springer, Berlin, Heidelberg
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