Future Frame Prediction Network for Video Anomaly Detection.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Traditional video anomaly detection relies on reconstruction errors, which can be insufficient for identifying abnormal events.
• Existing methods may struggle with self-reconstruction of normal events, leading to false negatives.

Purpose of the Study:

• To propose a new video anomaly detection framework based on video prediction.
• To develop principles for designing effective video prediction networks for anomaly detection.
• To enhance the generalization capability of prediction-based methods using meta-learning.

Main Methods:

• Formulating video anomaly detection as a video prediction problem.
• Designing a video prediction network incorporating appearance and motion constraints.
• Integrating meta-learning for rapid adaptation to new testing scenes.

Main Results:

• The proposed method demonstrates superior performance compared to reconstruction-based approaches.
• The model shows robustness to variations in normal events and sensitivity to abnormal events.
• Experiments on toy and real-world datasets validate the effectiveness of the approach.

Conclusions:

• Video prediction offers a more effective paradigm for anomaly detection than reconstruction error minimization.
• The designed network with specific constraints and meta-learning enhances detection accuracy and generalization.
• The method provides a robust solution for identifying unexpected events in videos.