Guess Future Anomalies from Normalcy: Forecasting Abnormal Behavior in Real-World Videos

Snehashis Majhi, Mohammed Guermal, Antitza Dantcheva, Quan Kong, Lorenzo Garattoni, Gianpiero Francesca, François Brémond; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 388-398

Forecasting Abnormal Human Behavior (AHB) aims to predict unusual behavior in advance by analyzing early patterns of normal human interactions. Unlike typical action prediction methods this task focuses on observing only normal interactions to predict both short and long term future abnormal behavior. Despite its affirmative impact on society AHB prediction remains under-explored in current research. This is primarily due to the challenges involved in anticipating complex human behaviors and interactions with surrounding agents in real-world situations. Further there exists an underlying uncertainty between the early normal patterns and the future abnormal behavior thereby making the prediction harder. To address these challenges we introduce a novel transformer model that improves early interaction modeling by accounting for uncertainties in both observations and future outcomes. To the best of our knowledge we are the first to explore the task. Therefore we present a new comprehensive dataset referred to as AHB-F which features real-world scenarios with complex human interactions. The AHB-F has a deterministic evaluation protocol that ensures only normal frames to be observed for long and short term future prediction. We extensively evaluate and compare competitive action anticipation methods on our benchmark. Our results show that our method consistently outperforms existing action anticipation approaches both in quantitative and qualitative evaluations.