Towards Hazardous Activity Recognition for A Novel Real-World Dataset

Detecting hazardous activities is essential for ensuring safety. However, existing datasets often lack coverage of the nuanced and diverse hazards present in indoor environments, which hinders the development of a specialized model. To address this, we introduce the Real-World Hazardous Activities Dataset (RHAD), a novel and diverse video dataset specifically curated for recognizing hazardous activities in real-world indoor settings. Leveraging RHAD, we introduce HazardNet, a hybrid deep-learning architecture designed for hazardous activity recognition. HazardNet integrates local and global spatial-temporal representation modules to effectively capture complex patterns, enabling a robust understanding of the activity. We perform comprehensive evaluations by benchmarking against a range of state-of-the-art activity recognition models. Experimental results show that our proposed model performs significantly better, surpassing the latest model, VideoMamba, with a 9.2% accuracy gain. Moreover, by providing the dataset and an effective recognition model, our work lays the foundation for further research, paving the way for enhanced safety measures and preventive interventions. The dataset and code are available at https://github.com/ShehzadCS18/RHAD.