Open AccessOccluded pedestrian detection combined with semantic features
Author(s) -
Ruan Binjie,
Zhang Chongyang
Publication year - 2021
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/ipr2.12196
Subject(s) - pedestrian detection , computer science , pedestrian , artificial intelligence , feature (linguistics) , computer vision , semantic feature , object detection , pattern recognition (psychology) , task (project management) , detector , feature extraction , geography , engineering , telecommunications , linguistics , philosophy , archaeology , systems engineering
Abstract The task of pedestrian detection is to identify the location and size of pedestrians in images or videos. However, occlusions are very common in real‐life scenarios, which make pedestrian detection more difficult. In order to solve the occlusion problem in pedestrian detection, a semantic feature enhancement module to acquire more informative and richer semantic features is proposed. The detector enhances semantic features by fusing feature maps of different layers, and detects pedestrians based on their locations and scales. The Experiments performed on Caltech and CityPersons datasets show that the algorithm achieves superior performance for detecting occluded pedestrians, especially heavily occluded ones. 30.6% and 47.9% log‐average missing rates are achieved in the heavily occluded subsets of Caltech and Cityperons, respectively. Moreover, this method is robust to the detection of heavily occluded pedestrians, and the module can be easily used by other detection frameworks.