Toward Robust Pedestrian Detection With Data Augmentation
In this article, the problem of creating a safe pedestrian detection model that can operate in the real world is tackled. While recent advances have led to significantly improved detection accuracy on various benchmarks, existing deep learning models are vulnerable to invisible to the human eye chan...
Main Authors: | Sebastian Cygert, Andrzej Czyzewski |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9146161/ |
Similar Items
-
Single Shot Multibox Detector With Kalman Filter for Online Pedestrian Detection in Video
by: Fan Yang, et al.
Published: (2019-01-01) -
Faster R-CNN for Robust Pedestrian Detection Using Semantic Segmentation Network
by: Tianrui Liu, et al.
Published: (2018-10-01) -
CSANet: Channel and Spatial Mixed Attention CNN for Pedestrian Detection
by: Yunbo Zhang, et al.
Published: (2020-01-01) -
Accelerate High Resolution Image Pedestrian Detection With Non-Pedestrian Area Estimation
by: Haodi Zhang, et al.
Published: (2021-01-01) -
Attention Based Multi-Layer Fusion of Multispectral Images for Pedestrian Detection
by: Yongtao Zhang, et al.
Published: (2020-01-01)