Open AccessPedestrian Recognition Based On Improved Semantic Segmentation Neural Network
Author(s) -
Xianping Guo,
Yaodong Wang
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 - 10.1088/1742-6596/1549/5/052036
Subject(s) - segmentation , computer science , artificial intelligence , pattern recognition (psychology) , feature (linguistics) , artificial neural network , image segmentation , scale space segmentation , semantic feature , computer vision , philosophy , linguistics
At present, the existing Re-ID methods can’t align the target and the target in the image that should to be detected, which has been affecting the recognition accuracy. Although using semantic segmentation method can solve this problem, it brings new problems. The region which is considered as background by semantic segmentation neural network and discarded is likely to contain feature representations that are helpful for recognition. In this paper, an improved semantic segmentation method is proposed to re-divide the foreground and background regions. In order to avoid the influence of semantic segmentation errors on recognition accuracy, a new method is used to combine background and foreground regions. Experiments show that the accuracy of Re-ID is 84.52%, which is 3.33% higher than before.