Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study

While action recognition (AR) has gained large improvements with the introduction of large-scale video datasets and the development of deep neural networks, AR models robust to challenging environments in real-world scenarios are still under-explored. We focus on the task of action recognition in da...

Full description

Saved in:
Bibliographic Details
Published in:International journal of computer vision 2024-04, Vol.132 (4), p.1292-1309
Main Authors: Xu, Yuecong, Cao, Haozhi, Yin, Jianxiong, Chen, Zhenghua, Li, Xiaoli, Li, Zhengguo, Xu, Qianwen, Yang, Jianfei
Format: Article
Language:English
Subjects:
Activity recognition
Artificial Intelligence
Artificial neural networks
Community participation
Computer Imaging
Computer Science
Datasets
Environment models
Image Processing and Computer Vision
Neural networks
Pattern Recognition
Pattern Recognition and Graphics
Robustness
Surveillance
Vision
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:While action recognition (AR) has gained large improvements with the introduction of large-scale video datasets and the development of deep neural networks, AR models robust to challenging environments in real-world scenarios are still under-explored. We focus on the task of action recognition in dark environments, which can be applied to fields such as surveillance and autonomous driving at night. Intuitively, current deep networks along with visual enhancement techniques should be able to handle AR in dark environments, however, it is observed that this is not always the case in practice. To dive deeper into exploring solutions for AR in dark environments, we launched the UG 2 + Challenge Track 2 (UG2-2) in IEEE CVPR 2021, with a goal of evaluating and advancing the robustness of AR models in dark environments. The challenge builds and expands on top of a novel ARID dataset, the first dataset for the task of dark video AR, and guides models to tackle such a task in both fully and semi-supervised manners. Baseline results utilizing current AR models and enhancement methods are reported, justifying the challenging nature of this task with substantial room for improvements. Thanks to the active participation from the research community, notable advances have been made in participants’ solutions, while analysis of these solutions helped better identify possible directions to tackle the challenge of AR in dark environments.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-023-01932-5