Eliminating False Alarms in Action Detection Frameworks

False predictions often hampered human action recognition in videos, reducing the reliability of detection models. This paper presents a novel approach that integrates Video Vision Transformer (ViViT) and YOLOv8 to minimize false alarms in action detection. YOLOv8 detects human subjects within video segments, while ViViT reclassifies these segments to reduce false positives. We validate our method on two benchmark datasets: THUMOS14 and EPIC-Kitchen. Our experiments substantially reduce false positives, improving model performance without sacrificing accuracy. Specifically, our framework reduces false predictions by 45.8This approach enhances the precision of action detection models, offering a more robust and reliable solution for practical applications such as video surveillance and human activity analysis in untrimmed videos.