Anomaly detection is a key functionality in various vision systems, such as surveillance and security. In this work, we present a convolutional neural network (CNN) that supports the detection of anomaly, which has not been defined when building the model, at frame level. Our CNN, named SmithNet, is structured to simultaneously learn commonly occurring textures and their corresponding motion. Its architecture is a combination of: 1) an encoder extracting motion-texture coherence from each video frame and 2) two decoders that separately reconstruct the input as well as predict its typical motion from the estimated coherence. We also introduce an encoding block, which is specifically designed for the task of anomaly detection. The optimization is performed on only data of normal events, and the network is expected to determine the ones that are unusual, i.e., have not been seen before. According to the experiments on eight benchmark datasets of different environments with various anomalous events, the performance of our network is competitive or outperforms current state-of-the-art approaches.

中文翻译：

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SmithNet：异常检测的运动纹理一致性的严格性


异常检测是各种视觉系统（例如监控和安全）的关键功能。在这项工作中，我们提出了一种卷积神经网络（CNN），它支持在帧级别检测异常，这些异常在构建模型时尚未定义。我们的 CNN 名为 SmithNet，其结构可以同时学习常见的纹理及其相应的运动。其架构由以下部分组合而成：1）一个编码器从每个视频帧中提取运动纹理一致性；2）两个解码器分别重建输入并根据估计的一致性预测其典型运动。我们还引入了一个编码块，它是专门为异常检测任务而设计的。仅对正常事件的数据进行优化，并且网络期望确定不寻常的数据，即以前没有见过的数据。根据对具有各种异常事件的不同环境的八个基准数据集的实验，我们的网络的性能具有竞争力或优于当前最先进的方法。