# A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras
## 5 Conclusions, Limitations and Future Work  
This paper defines a novel structure combining ResNet50 and ConvLSTM to detect abnormal behaviour in the UCF-Crime dataset. There are several limitations that we faced in implementing this model. The dataset we used is in different illumination, speed, and subjects. For instance, some anomalies happened in the video, while we could not see any person in some videos (i.e., car accidents). Besides, we need to deal with another limitation of our dataset. The abnormal events may only take one or two seconds to happen, and even in 10-second videos, more than 80 per cent of the video length shows it is normal behaviour. Despite all mentioned limitations, our proposed method outperforms other methods on the UCF-Crime dataset. In addition to using all 14 categories of UCF-Crime, binary classification, and dividing into four major categories, we also trimmed the original video of three different anomalous events. We have both abnormal and normal events with the same background and objects. We implemented one of the most popular CNNs, ResNet50, to extract the most critical features from each input video frame. Then, the output of each ResNet goes through a ConvLSTM structure to explore the abnormal event in a series of frames. Finally, we used classifiers for each dataset to figure out how the model accurately recognizes each input video's right category. Although the experimental results show that our approach did better than other exiting models, we are looking to improve classifying all 13 types of anomalies in the UCF-Crime dataset. One of the approaches is adding an attention layer to the structure that we will work on in the future. Therefore, this attention layer can be added to CNN structure and/or ConvLSTM. Thus, the model can focus more accurately on the happening anomalies in the video file.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Acc (\%)** \\ \hline ResNet50 ConvLSTM & 22.72 \\ ResNet101 ConvLSTM & 23.75 \\ C3D [29] & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy for 14 categories classification on the UCF-Crime dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **AUC (\%)** & **Acc (\%)** \\ \hline NREF & 79.04 & 65.38 \\
4 MajCat & 73.88 & 62.22 \\ Ucfcrimes & 53.88 & 22.72 \\ Binary & 81.71 & 62.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy and AUC for all four datasets variants.  
**Author Contributions:** Conceptualization, S.V. and K.Y.; methodology, S.V. and K.Y.; software, S.V.; validation, S.V.; formal analysis, S.V.; investigation, S.V.; resources, S.V.; data curation, S.V.; writing--original draft preparation, S.V.; writing--review and editing, S.V.; visualization, S.V.; supervision, K.Y.; project administration, K.Y.; funding acquisition, K.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), funding reference number DDG-2020-00034. Cette recherche a ete financee par le Conseil de recherches en sciences naturelles et en genie du Canada (CRSNG), numero de reference DDG-2020-00034.  
**Data Availability Statement:** The UCF-Crimes dataset includes 1900 long untrimmed videos captured by surveillance cameras in which, half of them contain real world abnormal events while the rest are normal videos. It covers 13 kinds of anomalies including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting, and Vandalism. The data presented in this study are available in [30].  
**Conflicts of Interest:** The authors declare no conflict of interest.