A Novel Lightweight Framework Using Zero-DCE and Epsilon Sampling Strategy for Improving Dark Object Recognition

Abstract

<p>The paper presents a novel lightweight framework for real-time human detection, tracking, and action recognition in low-light environments, addressing critical challenges of poor visibility and noise in surveillance and autonomous systems. Key problems tackled are insufficient image contrast, real-time detection misses, high computational load, and class imbalance. We propose: Zero-Reference Deep Curve Estimation (Zero-DCE) for dark region contrast enhancement; an optimized YOLOv5 detector with cascaded 3×3 max-pooling stacks; a core Epsilon sampling strategy ensuring temporal diversity and computational efficiency by strategically selecting frames to avoid omitting semantically critical content; a ResNet-34-based R(2+1)D network combined with a Transformer-style BERT module for robust action recognition under occlusion/low contrast; and a “deep compression” pipeline. Focused loss and data augmentation mitigate class imbalance. Evaluated on low-light datasets (ARID, HMDB51, synthetic HMDB51-dark), our framework achieves state-of-the-art performance. It reduces model size by 50% and inference latency by 28% while maintaining near-original accuracy.</p>