OwlSight: A Robust Illumination Adaptation Framework for Dark Video Human Action Recognition

Human action recognition in low-light environments is crucial for various real-world applications. However, the existing methods overlook the full utilization of brightness information throughout the training phase, leading to suboptimal performance. To address this issue, we propose OwlSight, a biomimetic framework with whole-stage illumination enhancement to interact with action classification for accurate dark video human action recognition. Specifically, OwlSight incorporates a Time-Consistency Module (TCM) to capture shallow spatiotemporal features meanwhile maintaining temporal coherence, which are then processed by a Luminance Adaptation Module (LAM) to dynamically adjust the brightness based on the input luminance distribution. Furthermore, a Reflect Augmentation Module (RAM) is presented to maximize illumination utilization and simultaneously enhance action recognition via two interactive paths. Additionally, we build a large-scale dataset Dark-101, which comprises 21,030 dark videos across 101 action categories, significantly surpassing the existing datasets (e.g., ARID1.5 and Dark-48) in scale and diversity. Our method establishes new state-of-the-art (SOTA) performance across all benchmarks, achieving Top-1 accuracies of 99.27% on ARID (a 2.0% improvement), 94.85% on ARID1.5 (a 5.36% improvement), 48.24% on Dark-48 (a 1.56% improvement), and 53.85% on Dark-101 (a 1.72% improvement), demonstrating its superior effectiveness in challenging dark video environments.