  Anomaly detection in videos is challenging due to the complexity, noise, and
diverse nature of activities such as violence, shoplifting, and vandalism.
While deep learning (DL) has shown excellent performance in this area, existing
approaches have struggled to apply DL models across different anomaly tasks
without extensive retraining. This repeated retraining is time-consuming,
computationally intensive, and unfair. To address this limitation, a new DL
framework is introduced in this study, consisting of three key components:
transfer learning to enhance feature generalization, model fusion to improve
feature representation, and multi-task classification to generalize the
classifier across multiple tasks without training from scratch when new task is
introduced. The framework's main advantage is its ability to generalize without
requiring retraining from scratch for each new task. Empirical evaluations
demonstrate the framework's effectiveness, achieving an accuracy of 97.99% on
the RLVS dataset (violence detection), 83.59% on the UCF dataset (shoplifting
detection), and 88.37% across both datasets using a single classifier without
retraining. Additionally, when tested on an unseen dataset, the framework
achieved an accuracy of 87.25%. The study also utilizes two explainability
tools to identify potential biases, ensuring robustness and fairness. This
research represents the first successful resolution of the generalization issue
in anomaly detection, marking a significant advancement in the field.
