  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.
