Open AccessPedestrian detection framework based on magnetic regional regression
Author(s) -
Yao Li,
Wang Bofan
Publication year - 2019
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/iet-ipr.2018.6193
Subject(s) - pedestrian , pedestrian detection , computer science , convolutional neural network , artificial intelligence , segmentation , regression , process (computing) , pattern recognition (psychology) , data mining , machine learning , geography , statistics , mathematics , archaeology , operating system
In recent years, pedestrian detection has become one of the core issues in self‐driving car and automatic alert in video surveillance. However, the efficiency and accuracy are not ideal in multi‐pedestrian scenarios because of the occlusion by other pedestrian or non‐human objects. Here, the authors design a pedestrian‐detection framework based on region proposal network and convolutional neural network. Magnetic region regression strategy was proposed to reduce locating errors and false detection in the process of region regression. Meanwhile, semantic segmentation is integrated into authors’ framework to improve the accuracy of classification. Additionally, the authors used soft‐ non‐maximum suppression (NMS) to reduce the impact of NMS threshold on the model. Authors’ framework has average miss rate of 6.94% on Improved Caltech‐USA dataset. The experiments show that authors’ framework achieves significant advantages.