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Cascade classifiers are widely used in real-time object detection. Differentfrom conventional classifiers that are designed for a low overallclassification error rate, a classifier in each node of the cascade is requiredto achieve an extremely high detection rate and moderate false positive rate.Although there are a few reported methods addressing this requirement in thecontext of object detection, there is no principled feature selection methodthat explicitly takes into account this asymmetric node learning objective. Weprovide such an algorithm here. We show that a special case of the biasedminimax probability machine has the same formulation as the linear asymmetricclassifier (LAC) of Wu et al (2005). We then design a new boosting algorithmthat directly optimizes the cost function of LAC. The resultingtotally-corrective boosting algorithm is implemented by the column generationtechnique in convex optimization. Experimental results on object detectionverify the effectiveness of the proposed boosting algorithm as a nodeclassifier in cascade object detection, and show performance better than thatof the current state-of-the-art.

Training Effective Node Classifiers for Cascade Classification