Cargando…
An Occlusion-Robust Feature Selection Framework in Pedestrian Detection †
Better features have been driving the progress of pedestrian detection over the past years. However, as features become richer and higher dimensional, noise and redundancy in the feature sets become bigger problems. These problems slow down learning and can even reduce the performance of the learned...
Autores principales: | Guo, Zhixin, Liao, Wenzhi, Xiao, Yifan, Veelaert, Peter, Philips, Wilfried |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068818/ https://www.ncbi.nlm.nih.gov/pubmed/30011869 http://dx.doi.org/10.3390/s18072272 |
Ejemplares similares
-
Probabilistic Fusion for Pedestrian Detection from Thermal and Colour Images
por: Shaikh, Zuhaib Ahmed, et al.
Publicado: (2022) -
Behavioral Pedestrian Tracking Using a Camera and LiDAR Sensors on a Moving Vehicle
por: Dimitrievski, Martin, et al.
Publicado: (2019) -
General Image Fusion for an Arbitrary Number of Inputs Using Convolutional Neural Networks
por: Xiao, Yifan, et al.
Publicado: (2022) -
Extrinsic Calibration of Camera Networks Based on Pedestrians
por: Guan, Junzhi, et al.
Publicado: (2016) -
An Unsupervised Transfer Learning Framework for Visible-Thermal Pedestrian Detection
por: Lyu, Chengjin, et al.
Publicado: (2022)