A Part-Aware Multi-Scale Fully Convolutional Network for Pedestrian Detection

Peiyu Yang, Guofeng Zhang, Lu Wang, Lisheng Xu, Qingxu Deng, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Pedestrian detection is a crucial task in intelligent transportation systems, which can be applied in autonomous vehicles and traffic scene video surveillance systems. The past few years have witnessed much progress on the research of pedestrian detection methods, especially through the successful use of the deep learning based techniques. However, occlusion and large scale variation remain the challenging issues for pedestrian detection. In this work, we propose a Part-Aware Multi-Scale Fully Convolutional Network (PAMS-FCN) to tackle these difficulties. Specifically, we present a part-aware Region-of-Interest (RoI) pooling module to mine body parts with different responses, and select the part with the strongest response via voting. As such, a partially visible pedestrian instance can receive a high detection confidence score, making it less likely to become a missing detection. This module operates in parallel with an instance RoI pooling module to combine local parts and global context information. To handle vast scale variation, we construct a fully convolutional network in which multi-scale feature maps are generated efficiently, and small-scale and large-scale pedestrians are detected separately. By integrating these structures, the proposed detector achieves the state-of-the-art performance on the Caltech, KITTI, INRIA and ETH pedestrian detection datasets.

Original languageEnglish
Article number8960286
Pages (from-to)1125-1137
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Volume22
Issue number2
DOIs
Publication statusPublished - 2021 Feb

Bibliographical note

Funding Information:
Manuscript received June 15, 2019; revised October 29, 2019; accepted December 17, 2019. Date of publication January 15, 2020; date of current version February 2, 2021. This work was supported in part by the Natural Science Foundation of Liaoning Province under Grant 20170540312, in part by the Fundamental Research Funds for the Central Universities of China under Grant N161602001 and Grant N181604006, and in part by the National Natural Science Foundation of China under Grant 61773110 and Grant U1908212. The Associate Editor for this article was Z. Duric. (Corresponding author: Lu Wang.) Peiyu Yang, Guofeng Zhang, Lu Wang, and Qingxu Deng are with the School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China, and also with the Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110819, China (e-mail: 1771472@stu.neu.edu.cn; 1771482@ stu.neu.edu.cn; wanglu@cse.neu.edu.cn; dengqx@mail.neu.edu.cn).

Publisher Copyright:
© 2000-2011 IEEE.

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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