Training Effective Node Classifiers for Cascade Classification
This work addresses a specific bottleneck in real-time object detection by providing a principled feature selection method for cascade classifiers, which is incremental but improves upon existing approaches.
The paper tackled the problem of designing effective node classifiers for cascade object detection by proposing a new boosting algorithm that directly optimizes the linear asymmetric classifier cost function, achieving better performance than state-of-the-art methods in experiments.
Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although there are a few reported methods addressing this requirement in the context of object detection, there is no principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such an algorithm here. We show that a special case of the biased minimax probability machine has the same formulation as the linear asymmetric classifier (LAC) of Wu et al (2005). We then design a new boosting algorithm that directly optimizes the cost function of LAC. The resulting totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on object detection verify the effectiveness of the proposed boosting algorithm as a node classifier in cascade object detection, and show performance better than that of the current state-of-the-art.