Abstract
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.
Abstract (translated)
日常生活中的暴力普遍存在，对个体的身体健康和心理健康构成严重威胁。在公共场所使用监控摄像头已经证明能够有效地预防并阻止此类事件的发生。然而，隐私侵犯问题也因为广泛使用监控摄像头而凸显出来。为了解决这一问题，我们利用动态视觉传感器(DVS)摄像头来检测暴力事件并保护隐私，因为它能够捕捉像素亮度的变化而不是静态图像。我们介绍了“欺凌10K数据集”，涵盖了各种行动、复杂动作和遮挡情况，提供了三个基准任务以评估不同的任务：行动识别、时间动作定位和姿态估计。该数据集有10,000个事件片段，总共涵盖了120亿事件和2.55GB的数据，通过平衡暴力检测和个人隐私坚持， significantly contribute to these domains' innovative approaches. 该数据集也挑战了神经可塑性数据集。它将成为训练和开发保护隐私的视频系统的有价值的资源。欺凌10K为这些领域的创新方法带来了新的可能性。
URL
https://arxiv.org/abs/2306.11546
