用深度嵌入联合优化网络与异常检测，区分正常与异常视频。
Abnormal Event Detection In Videos Using Deep Embedding
- 通过融合深度、运动和外观特征预训练自编码器
- 将正常视频嵌入聚集在超中心附近，异常则远离
- 无需标注数据，适合安防监控场景
视频异常检测因事件多样性而困难，且训练时缺乏异常样本，需无监督学习方法。本文提出一种无监督视频异常检测方法，采用混合架构联合优化深度神经网络与异常检测目标。首先，使用深度、运动和外观特征融合的卷积自编码器进行无监督预训练；其次，利用预训练编码器提取融合输入的嵌入表示，并联合微调编码器，使其将嵌入映射到一个超中心。正常数据的嵌入聚集在超中心附近，异常数据则远离该中心。实验表明，该方法在UCF-Crime、ShanghaiTech等数据集上表现优异，相比基线提升显著。
原文摘要 · Abstract (English)
Abnormal event detection or anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without supervision. In this work we propose an unsupervised approach for video anomaly detection with the aim to jointly optimize the objectives of the deep neural network and the anomaly detection task using a hybrid architecture. Initially, a convolutional autoencoder is pre-trained in an unsupervised manner with a fusion of depth, motion and appearance features. In the second step, we utilize the encoder part of the pre-trained autoencoder and extract the embeddings of the fused input. Now, we jointly train/ fine tune the encoder to map the embeddings to a hypercenter. Thus, embeddings of normal data fall near the hypercenter, whereas embeddings of anomalous data fall far away from the hypercenter.
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