Implicit hierarchical boosting for multi-view object detection

Multi-view object detection is a fundamental problem in computer vision. Current approaches generally require an explicit partition between different views with or without sharing descriptors. We present a novel boosting based learning approach which automatically learns a multi-view detector without using intra-class sub-categorization based on prior knowledge. To avoid multiplying the false alarm rate by the number of classifiers, which happens on the classical approach where one classifier per view is considered, we build a single cascade of weak classifiers which contains an implicit hierarchical structure. In details, a partition of positive samples is automatically computed in order to build an adequate weak classifier based on one specific descriptor per subset. By adapting iteratively the number of descriptors at each stage, the so-defined hierarchical structure enables both a precise modelling and an efficient sharing of descriptors between views. Experimental results demonstrate the relevance and efficiency of this new approach.