A Speed-Up Scheme Based on Multiple-Instance Pruning for Pedestrian Detection Using a Support Vector Machine

In pedestrian detection, as sophisticated feature descriptors are used for improving detection accuracy, its processing speed becomes a critical issue. In this paper, we propose a novel speed-up scheme based on multiple-instance pruning (MIP), one of the soft cascade methods, to enhance the processing speed of support vector machine (SVM) classifiers. Our scheme mainly consists of three steps. First, we regularly split an SVM classifier into multiple parts and build a cascade structure using them. Next, we rearrange the cascade structure for enhancing the rejection rate, and then train the rejection threshold of each stage composing the cascade structure using the MIP. To verify the validity of our scheme, we apply it to a pedestrian classifier using co-occurrence histograms of oriented gradients trained by an SVM, and experimental results show that the processing time for classification of the proposed scheme is as low as one-hundredth of the original classifier without sacrificing detection accuracy.