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Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
Content Provider | MDPI |
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Author | Xie, Han Zheng, Wenqi Shin, Hyunchul |
Copyright Year | 2021 |
Description | Although many deep-learning-based methods have achieved considerable detection performance for pedestrians with high visibility, their overall performances are still far from satisfactory, especially when heavily occluded instances are included. In this research, we have developed a novel pedestrian detector using a deformable attention-guided network (DAGN). Considering that pedestrians may be deformed with occlusions or under diverse poses, we have designed a deformable convolution with an attention module (DCAM) to sample from non-rigid locations, and obtained the attention feature map by aggregating global context information. Furthermore, the loss function was optimized to get accurate detection bounding boxes, by adopting complete-IoU loss for regression, and the distance IoU-NMS was used to refine the predicted boxes. Finally, a preprocessing technique based on tone mapping was applied to cope with the low visibility cases due to poor illumination. Extensive evaluations were conducted on three popular traffic datasets. Our method could decrease the log-average miss rate ( |
Starting Page | 6025 |
e-ISSN | 20763417 |
DOI | 10.3390/app11136025 |
Journal | Applied Sciences |
Issue Number | 13 |
Volume Number | 11 |
Language | English |
Publisher | MDPI |
Publisher Date | 2021-06-29 |
Access Restriction | Open |
Subject Keyword | Applied Sciences Transportation Science and Technology Pedestrian Detection Feature Extraction Computer Vision Image Processing |
Content Type | Text |
Resource Type | Article |