Action/Event Detection
License: Unknown


The task of action recognition in dark videos is useful in various scenarios, e.g., night surveillance and self-driving at night. Though progress has been made in action recognition task for videos in normal illumination, few have studied action recognition in the dark. This is partly due to the lack of sufficient datasets for such a task. In this paper, we explored the task of action recognition in dark videos. We bridge the gap of the lack of data for this task by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset. It consists of over 3,780 video clips with 11 action categories. To the best of our knowledge, it is the first dataset focused on human actions in dark videos. To gain further understanding of our ARID dataset, we analyze the ARID dataset in detail and showed its necessity over synthetic dark videos. Additionally, we benchmark the performance of several current action recognition models on our dataset and explored potential methods for increasing their performances. Our results show that current action recognition models and frame enhancement methods may not be effective solutions for the task of action recognition in dark videos.


Please use the following citation when referencing the dataset:

title={ARID: A New Dataset for Recognizing Action in the Dark},
author={Xu, Yuecong and Yang, Jianfei and Cao, Haozhi and Mao, Kezhi and Yin, Jianxiong and
See, Simon},
journal={arXiv preprint arXiv:2006.03876},
Data Summary
Provided by
Nanyang Technological University, Singapore
The School of Electrical and Electronic Engineering (NTU EEE) began as one of the three founding schools of Nanyang Technological University, then known as Nanyang Technological Institute.
Start Building AI Now