Open AccessDeep learning for occluded and multi‐scale pedestrian detection: A review
Author(s) -
Xiao Yanqiu,
Zhou Kun,
Cui Guangzhen,
Jia Lianhui,
Fang Zhanpeng,
Yang Xianchao,
Xia Qiongpei
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12042
Subject(s) - pedestrian detection , pedestrian , deep learning , computer science , artificial intelligence , scale (ratio) , field (mathematics) , machine learning , computer vision , engineering , transport engineering , geography , cartography , mathematics , pure mathematics
Pedestrian detection, as a research hotspot in the field of computer vision, is widely used in many fields, such as automatic driving, video surveillance, robots and so on. In recent years, with the rapid development of deep learning, pedestrian detection technology has made unprecedented breakthroughs. However, it fails to saturate pedestrian detection research, and there are still many problems to be solved. This study reviews the current research status of pedestrian detection methods based on deep learning. In the first place, we summarised the research results of two stage and one stage pedestrian detection based on deep learning, also summarised and analysed the improvement methods. Meanwhile, we focused on the occlusion and multi‐scale problems of pedestrian detection and discussed the corresponding solutions. At last, we induced the pedestrian detection datasets and evaluation methods and prospected the development trend of deep learning in pedestrian detection.