In this paper, we propose a comprehensive framework for detecting anomalies in videos based on autoencoder (AE). Traditional AE models solely rely on input and final reconstruction, potentially limiting their capacity to fully utilize the intermediate neural network layers. To mitigate this limitation, we introduce a novel approach that concurrently trains the model using corresponding intermediate layers from both the encoder and decoder. This allows the model to capture more intricate features, thus enhancing its anomaly detection capabilities. Furthermore, we introduce a motion loss function that exclusively relies on original video frames rather than optical flow, rendering it more efficient and capable of extracting motion features. Additionally, we have devised a variance attention strategy that is parameter-free and can automatically directs our model's focus towards moving objects, further boosting the performance of our approach. Our experiments on three public datasets demonstrate the effectiveness and efficiency of our method in identifying abnormal events in complex scenarios. The code is publicly available at .

中文翻译：

---

基于具有方差注意策略的多层重建自动编码器的视频异常检测

在本文中，我们提出了一个基于自动编码器（AE）的视频异常检测综合框架。传统的 AE 模型仅依赖于输入和最终重建，这可能限制了它们充分利用中间神经网络层的能力。为了减轻这一限制，我们引入了一种新颖的方法，该方法使用编码器和解码器的相应中间层同时训练模型。这使得模型能够捕获更复杂的特征，从而增强其异常检测能力。此外，我们引入了一种运动损失函数，该函数完全依赖于原始视频帧而不是光流，使其更加高效并且能够提取运动特征。此外，我们设计了一种无参数的方差注意策略，可以自动将我们的模型的焦点转向移动物体，进一步提高我们方法的性能。我们对三个公共数据集的实验证明了我们的方法在识别复杂场景中的异常事件方面的有效性和效率。该代码可在 公开获取。