# Ensemble-Based Knowledge Distillation for Video Anomaly Detection
## 1 Introduction  
Recently, video surveillance systems have become the center of attention as they are deployed and utilized in nearly every place of human settlements for critical tasks such as public protection, crowd analysis and crime detection. However, these systems produce a substantial rate of video data that cannot be analyzed solely by the human factor. That is where the video anomaly detection task becomes prominent as it inherently helps to alleviate the burden of processing many critical surveillance tasks by focusing on abnormal scenes, consequently reducing the data to be analyzed by human operators. Similar to the general anomaly detection task, the purpose of the video anomaly detection is detecting anomaly patterns in the videos [1]. However, the implementation of this detection process can be very problematic because there is no exact or fixed detection pattern for generalizing all abnormal scenes in videos [1; 2; 3; 4]. Different network structures have been proposed for this task [2; 3; 5; 6]. Newer deep learning-based approaches have much more performance potential compared to their classical machine learning or statistical counterpart approaches because of their superior structure for capturing more complex low and high level details such as lighting, texture, background scenes, behavioral patterns [7] or general appearance and motion-based features [8; 9] in videos. But ultimately there is no all-in-one model capable of detecting all different anomaly patterns [10; 11]. Additionally, as the new video data are received, the models need to adapt to different complex scenes and capture new anomaly patterns. Since the video anomaly concept cannot be defined with specific patterns, the detection mechanism should be based on a data-driven approach; it should adapt itself to new anomaly patterns in the scenes in time by also remembering old patterns learned previously. A possible solution can be constantly retraining the models with all video data (including new video data), but this solution is not feasible as it brings a heavy computational burden on systems. Another possible solution could be continuously fine-tuning the models with new video data, but this solution is prone to the potential problem of forgetting old learned patterns in time, which is also called catastrophic forgetting. To alleviate this problem, a knowledge distillation method has been proposed, in which a student model can adapt itself to new video data by using a teacher model's feedback and ground truth annotations. In this way, the student model learns from new data while reducing the catastrophic forgetting problem. Moreover, the student model might learn from multiple networks such as in [12] and can combine different kinds of feedback from teacher models having different network structures or trained on different datasets. Consequently, we think that a separate adaptation mechanism is crucial for a model in a specific scenario, where the model has to prepare itself to constantly learn the new patterns (anomalous or normal) belonging to incoming video samples while the model must also not forget its previously learned patterns on a continuously expanding dataset in time. In this study, we build our motivation and novelty on developing such an adaptation mechanism on weakly supervised video anomaly detection tasks. We also set our objectives and our experiments for evaluating how feasible our proposed approaches are to integrate such an adaptation mechanism. **Proposed Work and Contributions:** With this motivation, we extend the knowledge distillation concept into a promising adaption mechanism utilizing information transfer from multiple teacher models having different network structures or trained on different datasets, while alleviating catastrophic forgetting problems. In this paper, we propose three ensemble-based knowledge distillation approaches for video anomaly detection. As a baseline model, we deploy two state-of-the-art model structures, which are the AR-Net model [13] and GCN model [14].