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

• URL: http://arxiv.org/abs/2503.23266v1
• Date: Sun, 30 Mar 2025 00:54:22 GMT
• Title: OwlSight: A Robust Illumination Adaptation Framework for Dark Video Human Action Recognition
• Authors: Shihao Cheng, Jinlu Zhang, Yue Liu, Zhigang Tu,
• Abstract summary: We propose OwlSight, a biomimetic-inspired framework with whole-stage illumination enhancement to interact with classification action for accurate dark video human action recognition.<n>We build Dark-101, a large-scale dataset comprising 18,310 dark videos across 101 action categories, significantly surpassing existing datasets in scale and diversity.<n> Notably, it outperforms previous best approaches by 5.36% on ARID1.5 and 1.72% on Dark-101, highlighting its effectiveness in challenging dark environments.
• Score: 19.035892288559975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human action recognition in low-light environments is crucial for various real-world applications. However, the existing approaches overlook the full utilization of brightness information throughout the training phase, leading to suboptimal performance. To address this limitation, we propose OwlSight, a biomimetic-inspired 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 Dark-101, a large-scale dataset comprising 18,310 dark videos across 101 action categories, significantly surpassing existing datasets (e.g., ARID1.5 and Dark-48) in scale and diversity. Extensive experiments demonstrate that the proposed OwlSight achieves state-of-the-art performance across four low-light action recognition benchmarks. Notably, it outperforms previous best approaches by 5.36% on ARID1.5 and 1.72% on Dark-101, highlighting its effectiveness in challenging dark environments.

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