Context-aware Pedestrian Detection with Salient Region Self-growing in Far-infrared Images

DOI

10.2991/acaai-18.2018.18How to use a DOI?

Keywords

pedestrian detection; infrared; neural network

Abstract

In this paper, we present a new framework to detect pedestrians in infrared images. The framework consists of a candidate generation module and a classification module, both of which are implemented based on convolution neural network. Specifically, we learned effective segmentation threshold by deep learning methods, and proposed a salient region self-growing algorithm to generate candidates. Besides, we conducted context-aware classification on the candidates to reduce the false positives using cues from the context. We achieved state-of-the-art result on a public dataset, which has shown the effectiveness of the proposed method.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hao Sheng
AU  - Meiyuan Liu
AU  - Yanwei Zheng
AU  - Yang Liu
PY  - 2018/03
DA  - 2018/03
TI  - Context-aware Pedestrian Detection with Salient Region Self-growing in Far-infrared Images
BT  - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)
PB  - Atlantis Press
SP  - 72
EP  - 76
SN  - 1951-6851
UR  - https://doi.org/10.2991/acaai-18.2018.18
DO  - 10.2991/acaai-18.2018.18
ID  - Sheng2018/03
ER  -