Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performance

ArXi:2410.18717v2 Announce Type: replace Recent advancements in artificial intelligence hold ample potential for monitoring applications using surveillance cameras. However, concerns about privacy and model bias have made it challenging to utilize them in public. Although de-identification approaches have been proposed in the literature, aiming to achieve a certain level of anonymization (AN), most of them employ deep learning models that are computationally demanding for real-time edge deployment.