Violence Detection (VD) has become an increasingly vital area of research. Existing automated VD efforts are hindered by the limited availability of diverse, well-annotated databases. Existing databases suffer from coarse video-level annotations, limited scale and diversity, and lack of metadata, restricting the generalization of models. To address these challenges, we introduce DVD, a large-scale (500 videos, 2.7M frames), frame-level annotated VD database with diverse environments, varying lighting conditions, multiple camera sources, complex social interactions, and rich metadata. DVD is designed to capture the complexities of real-world violent events.
@article{arxiv.2506.05372,
title = {DVD: A Comprehensive Dataset for Advancing Violence Detection in Real-World Scenarios},
author = {Dimitrios Kollias and Damith C. Senadeera and Jianian Zheng and Kaushal K. K. Yadav and Greg Slabaugh and Muhammad Awais and Xiaoyun Yang},
journal= {arXiv preprint arXiv:2506.05372},
year = {2025}
}