Context-Aware Video Anomaly Detection in Long-Term Datasets with Temporal and Spatial Contextual Awareness

The core message of this work is to propose a context-aware video anomaly detection algorithm, Trinity, that can effectively detect anomalies in long-term video datasets by learning alignments between video content (appearance and motion) and contextual information (time of day, day of week, game schedule, etc.).