Abstract
We present a new method for object detection that integrates part-based model with cascades of boosted classifiers. The parts are labeled in a supervised manner. For each part, we construct a boosted cascade by selecting the most important features from a large set and combining more complex classifiers. The weak learners used in each level of the cascade are gradient features of variable-size blocks. Moreover, we learn a model of the spatial relations between those parts. In detection, the cascade of classifiers for each part compute the part values within all sliding windows and then the object is localized within the image by integrating the spatial relations model. The experimental results demonstrate that training a cascade of boosted classifiers for each part and adding spatial constraints among parts improve performance of detection and localization.
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Xia, X., Yang, W., Li, H., Zhang, S. (2010). Part-Based Object Detection Using Cascades of Boosted Classifiers. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_52
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DOI: https://doi.org/10.1007/978-3-642-12304-7_52
Publisher Name: Springer, Berlin, Heidelberg
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