4 papers
Localizing to Debias: A Patch-Level Benchmark and Baseline for Weakly Supervised Spatial Anomaly Detection
Sara Abdulaziz, Abdulrahman Al-Abri, Giacomo D'Amicantonio +1
Despite growing interest in weakly supervised video anomaly detection (WSVAD), current methods struggle to bridge the gap between coarse temporal supervision and fine-grained spati…
Auditing Frame-Level AUC in Weakly Supervised Video Anomaly Detection: Granularity, Resolution, and Scene Bias
Sara Abdulaziz, Egor Bondarev
Frame-level area under the ROC curve (AUC) is the dominant evaluation metric for weakly supervised video anomaly detection (WSVAD). Its standard form measures whether an anomalous…
Unmasking Performance Gaps: A Comparative Study of Human Anonymization and Its Effects on Video Anomaly Detection
Sara Abdulaziz, Egor Bondarev
Advancements in deep learning have improved anomaly detection in surveillance videos, yet they raise urgent privacy concerns due to the collection of sensitive human data. In this…
Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Dataset
Sara Abdulaziz, Giacomo D'Amicantonio, Egor Bondarev
Recent advances in AI-powered surveillance have intensified concerns over the collection and processing of sensitive personal data. In response, research has increasingly focused o…