Accelerate proposal generation in R-CNN methods for fast pedestrian extraction. Issue 3 (27th June 2019)

• Record Type: Journal Article
• Authors: Wang, Juncheng; Li, Guiying
• Is Part Of: Electronic library. Volume 37:Issue 3(2019)
• Page Start: 435
• Page End: 453
• Publication Date: 2019-06-27
• Subjects: Object proposal -- Object detection -- Convolutional neural network -- R-CNN, Computational efficiency -- Deep learning; Digital libraries -- Periodicals; Libraries -- Automation -- Periodicals; 025.00285
• DOI: 10.1108/EL-09-2018-0191
• Languages: English
• ISSNs: 0264-0473

Abstract

Abstract : Purpose: The purpose of this study is to develop a novel region-based convolutional neural networks (R-CNN) approach that is more efficient while at least as accurate as existing R-CNN methods. In this way, the proposed method, namely R 2 -CNN, provides a more powerful tool for pedestrian extraction for person re-identification, which involve a huge number of images and pedestrian needs to be extracted efficiently to meet the real-time requirement. Design/methodology/approach: The proposed R 2 -CNN is tested on two types of data sets. The first one the USC Pedestrian Detection data set, which consists of three sub-sets USC-A, UCS-B and USC-C, with respect to their characteristics. This data set is used to test the performance of R 2 -CNN in the pedestrian extraction task. The speed and performance of the investigated algorithms were collected. The second data set is the PASCAL VOC 2007 data set, which is a common benchmark data set for object detection. This data set was used to analyze characteristics of R 2 -CNN in the case of general object detection task. Findings: This study proposes a novel R-CNN method that is both more efficient and more accurate than existing methods. The method, when used as an object detector, would facilitate the data preprocessing stage of person re-identification. Originality/value: The study proposes a novel approach for object detection, which shows advantages in both efficiency and accuracy for pedestrian detection task. It contributes to both data preprocessing for person re-identification and the research on deep learning.