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The prevalence of violence in daily life poses significant threats toindividuals' physical and mental well-being. Using surveillance cameras inpublic spaces has proven effective in proactively deterring and preventing suchincidents. However, concerns regarding privacy invasion have emerged due totheir widespread deployment. To address the problem, we leverage Dynamic VisionSensors (DVS) cameras to detect violent incidents and preserve privacy since itcaptures pixel brightness variations instead of static imagery. We introducethe Bullying10K dataset, encompassing various actions, complex movements, andocclusions from real-life scenarios. It provides three benchmarks forevaluating different tasks: action recognition, temporal action localization,and pose estimation. With 10,000 event segments, totaling 12 billion events and255 GB of data, Bullying10K contributes significantly by balancing violencedetection and personal privacy persevering. And it also poses a challenge tothe neuromorphic dataset. It will serve as a valuable resource for training anddeveloping privacy-protecting video systems. The Bullying10K opens newpossibilities for innovative approaches in these domains.

Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition