# An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders
## 5 Conclusions  
In this paper, we introduce a novel unsupervised learning approach for a video anomaly detection system based on convolutional auto-encoder architectures. We focus on the anomalies that occur in outdoor scenes, considering the challenging publicly available UCSD anomaly detection datasets. The fundamental advantage of our approach is the use of a variational convolutional auto-encoder. On the one hand, our approach can extract features independent of the prior knowledge of hand-crafted features (the input of our detection system are raw pixels) and dispenses with any object-level analysis, like object detection and tracking. On the other hand, we omit the process of cropping the input into patches by the convolution principle of the convolutional neural network, which makes our framework simple and clear. We demonstrate the effectiveness and robustness of the proposed approach, showing competitive performance to existing methods.  
In fact, the approcah we present for the anomaly detection system can be viewed as a baseline of using a variational auto-encoder to detect anomalies in surveillance video. Further research directions will include jointing the input with richer temporal and contextual information and combining the feature extraction with the final anomaly decision. Besides, we can learn from the deep neural network frameworks for object detection and classification tasks to design more sophisticated frameworks, in order to represent the multiple patterns from the input video.  
All the authors made significant contributions to this work. Conceptualization, M.X. and D.C.; methodology, M.X. and D.C.; validation, M.X., X.Y. and C.W.; formal analysis, M.X. and X.Y.; investigation, M.X.; writing--original draft preparation, M.X.; writing--review and editing, M.X., D.C. and C.W.; supervision, C.W. and Y.J.  
This research was funded by National Natural Science Foundation of China under Grant 61701101, U1713216 and U1613214, National Key R&D Program of China under Grant 2017 YFC0821402, National Key  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Conv-AE**[40] & **FFP**[6] & **Ours** \\ \hline ShanghaiTech & 0.61 & 0.73 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: AUC comparison on the ShanghaiTech dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **CPU** & **GPU** & **Running Time Ped1 (s)** & **Running Time Ped2 (s)** \\ \hline MDT [32] & 3.0 GHz & - & 25 & - \\ SCLF [36] & 3.4 GHz & - & 0.00697 & - \\ AMDN [21] & 2.1 GHz & Nvidia Quadro K4000 & 5.2 & 7.5 \\ Ours without GPU & 2.1 GHz & - & 2.68 & 6.84 \\ Ours with GPU & 2.1 GHz & Nvidia TITANX & 0.00242 & 0.00265 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different methods in terms of running time (seconds per frame).  
Robot Project under Grant 2017 YFB1300900 and 2017 YFB1301103, Fundamental Research Fund for the Central Universities of China N172603001, N172604004, N172604002 and N181602014.  
We are thankful to anonymous editor and reviewers for their valuable comments and kind suggestions in early versions of this article.  
The authors declare no conflict of interest.