Open AccessPedestrian Detection in Crowded Crowd Scene Based on Deep Learning
Author(s) -
Zhenbin Le,
Zhaohui Meng
Publication year - 2021
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 - 10.1088/1742-6596/1744/4/042109
Subject(s) - pedestrian detection , pedestrian , artificial intelligence , computer science , computer vision , population , detector , object detection , pattern recognition (psychology) , geography , demography , archaeology , sociology , telecommunications
Detection-based methods typically detect and locate each person on a crowd image by using a designed pedestrian target detector and obtain counting results by accumulating each detected person. However, these methods require a large amount of computational resources and are often limited by human occlusion and complex background in real scenes, resulting in inaccurate detection. Based on the characteristics of computer depth learning and population density distribution map, a more optimized pedestrian detection approach is proposed.