  Human action recognition in dark videos is a challenging task for computer
vision. Recent research focuses on applying dark enhancement methods to improve
the visibility of the video. However, such video processing results in the loss
of critical information in the original (un-enhanced) video. Conversely,
traditional two-stream methods are capable of learning information from both
original and processed videos, but it can lead to a significant increase in the
computational cost during the inference phase in the task of video
classification. To address these challenges, we propose a novel teacher-student
video classification framework, named Dual-Light KnowleDge Distillation for
Action Recognition in the Dark (DL-KDD). This framework enables the model to
learn from both original and enhanced video without introducing additional
computational cost during inference. Specifically, DL-KDD utilizes the strategy
of knowledge distillation during training. The teacher model is trained with
enhanced video, and the student model is trained with both the original video
and the soft target generated by the teacher model. This teacher-student
framework allows the student model to predict action using only the original
input video during inference. In our experiments, the proposed DL-KDD framework
outperforms state-of-the-art methods on the ARID, ARID V1.5, and Dark-48
datasets. We achieve the best performance on each dataset and up to a 4.18%
improvement on Dark-48, using only original video inputs, thus avoiding the use
of two-stream framework or enhancement modules for inference. We further
validate the effectiveness of the distillation strategy in ablative
experiments. The results highlight the advantages of our knowledge distillation
framework in dark human action recognition.
