MIYAMOTO Ryusuke    Department   Undergraduate School  , School of Science and Technology    Position   Associate Professor
Language	English
Publication Date	2013/12
Type	Academic Journal
Peer Review	Peer reviewed
Title	A Speed-Up Scheme Based on Multiple-Instance Pruning for Pedestrian Detection Using a Support Vector Machine
Contribution Type	Co-authored (other than first author)
Journal	IEEE Trans. on Image Processing
Publisher	IEEE
Volume, Issue, Page	22(12),pp.4752-4761
Author and coauthor	Jaehoon Yu, Ryusuke Miyamoto, and Takao Onoye
Details	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.