The prevalence of violence in daily life poses significant threats to individuals' physical and mental well-being. Using surveillance cameras in public spaces has proven effective in proactively deterring and preventing such incidents. However, concerns regarding privacy invasion have emerged due to their widespread deployment. To address the problem, we leverage Dynamic Vision Sensors (DVS) cameras to detect violent incidents and preserve privacy since it captures pixel brightness variations instead of static imagery. We introduce the Bullying10K dataset, encompassing various actions, complex movements, and occlusions from real-life scenarios. It provides three benchmarks for evaluating different tasks: action recognition, temporal action localization, and pose estimation. With 10,000 event segments, totaling 12 billion events and 255 GB of data, Bullying10K contributes significantly by balancing violence detection and personal privacy persevering. And it also poses a challenge to the neuromorphic dataset. It will serve as a valuable resource for training and developing privacy-protecting video systems. The Bullying10K opens new possibilities for innovative approaches in these domains.

翻译：日常生活中暴力的普遍存在对个体的身心健康构成严重威胁。在公共场所使用监控摄像头已被证明能有效主动威慑和预防此类事件。然而，其广泛部署引发了关于隐私侵犯的担忧。为解决这一问题，我们利用动态视觉传感器（DVS）摄像头检测暴力事件并保护隐私，因为它捕捉的是像素亮度变化而非静态影像。我们引入了Bullying10K数据集，涵盖现实场景中的各种动作、复杂运动和遮挡。该数据集为评估不同任务提供了三个基准：动作识别、时序动作定位和姿态估计。Bullying10K包含10,000个事件片段，总计120亿个事件和255 GB数据，通过在暴力检测与个人隐私保护之间取得平衡做出了重要贡献，同时也对神经形态数据集提出了挑战。它将成为训练和开发隐私保护视频系统的宝贵资源。Bullying10K为这些领域的创新方法开辟了新的可能性。