Efficient Boosted Weak Classifiers for Object Detection

This paper accelerates boosted nonlinear weak classifiers in boosting framework for object detection. Although conventional nonlinear classifiers are usually more powerful than linear ones, few existing methods integrate them into boosting framework as weak classifiers owing to the highly computational cost. To address this problem, this paper proposes a novel nonlinear weak classifier named Partition Vector weak Classifier (PVC), which is based on the histogram intersection kernel functions of the feature vector with respect to a set of pre-defined Partition Vectors (PVs). A three-step algorithm is derived from the kernel trick for efficient weak learning. The obtained PVCs are further accelerated via building a look-up table. Experimental results in the detection tasks for multiple classes of objects show that boosted PVCs significantly improves both learning and evaluation efficiency of nonlinear SVMs to the level of boosted linear classifiers, without losing any of the high discriminative power.