Crowd Anomaly Detection From Drone and Ground

Video anomaly detection (VAD) is crucial for intelligent video surveillance systems. Many VAD methods for crowds in public spaces have been evaluated using videos captured by fixed cameras. Recently, drones have also been used for video surveillance, but the datasets of human activity captured by drones are relatively small or include actions by only a small number of people. Therefore, we created a large-scale dataset called Crowd Anomaly detection from Drone and Ground (CADG). A moving drone and two fixed ground cameras captured $448\times 3$ clips, comprising 236K $\times 3$ frames, of five types of crowd activities crucial to video surveillance. We also propose a unified learning framework for simultaneous anomaly detection and classification using a simple yet strong and computationally inexpensive temporal encoder. Experiments on the dataset show that the method detects crowd anomalies more accurately than conventional methods, but still highlight the difficulties of stable VAD from a moving drone and bridging the domain gap between views. The dataset is available on the project page at https://cadg24.github.io/home.