Open AccessLong-distanceinfrared video pedestrian detection using deep learning and backgroundsubtraction
Author(s) -
Yaling Zhu,
Jungang Yang,
Xiaokai Xieg,
Zhihui Wang,
Xinpu Deng
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1682/1/012012
Subject(s) - pedestrian detection , computer science , artificial intelligence , background subtraction , pedestrian , computer vision , object detection , deep learning , frame (networking) , infrared , pattern recognition (psychology) , pixel , telecommunications , engineering , transport engineering , physics , optics
Infrared video-based pedestrian detectionplays an important role in surveillance and automatic driving. Compared with vision cameras, infrared cameras have the characteristics of all-day and all-weather availability. The current infrared video detection methods generally follow the traditional paradigm. However, when objectsare at a long distance, it is challenging for existing methods to achieve accurate detection. Meanwhile, deep learning-based methods have been widely used in visible light object detection. In this paper, we introduce an algorithm which combines deep learning and background subtraction methods to achieve infrared pedestrian detection. Firstly, background subtraction method is performed to provide the inter-frame information for the deep learning module. Secondly, the RefineDet equipped with an attention module is used to improve the detection accuracy for small pedestrian. Moreover, we develop a novel infrared video dataset which includes long-distance pedestrian for performance evaluation. Experiments demonstrated that our method can achieve a superior performance as compared to RefinDetand SSD methods.