Context-aware Pedestrian Detection with Salient Region Self-growing in Far-infrared Images
Hao Sheng, Meiyuan Liu, Yanwei Zheng, Yang Liu
Available Online March 2018.
- 10.2991/acaai-18.2018.18How to use a DOI?
- pedestrian detection; infrared; neural network
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.
- © 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/).
Cite this article
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 -