# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 6 Conclusions and Future Work  
In this paper, a deep learning-based approach for the detection of falling, loitering, and violence in uncrowded scenes was presented. The paper proposed a two-stream CNN architecture that adopts an Inception 3D network for each of the spatial and temporal streams. The approach can extract spatiotemporal information and ensure the full use of motion information to achieve higher performance. Moreover, a successful application of transfer learning was presented that adopted the existing action recognition features and carried out specialized abnormal behavior detection. The experiments on the specialized  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Paper** & **Pretraining** & **Dataset** & **Data Used** & \
\begin{tabular}{c} **Method/Classification** \\ **Approach** \\ \end{tabular} & **Precision** & **Recall** & **Accuracy** \\ \hline [PERSON] et al. [17] & - & Hockey-DS & RGB & 3D-CNN & - & - & 0.89 \\ \hline [PERSON] et al. [2018] & - & Hockey-DS & RGB & STIP+MoSIFT+HIK & - & - & 0.91 \\ \cline{3-6}  & & Movies-DS & & - & - & 0.89 \\ \hline [PERSON] et al. & - & Hockey-DS & & & & 0.98 \\ \cline{3-6}  & & Movies-DS & & & - & 0.99 \\ \cline{3-6}  & & Crowd Violence & RGB &
\begin{tabular}{c} Feature fusion+2D- \\ CNN+LSTM \\ \end{tabular} & & & & 0.97 \\ \cline{3-6}  & & BEHAVE & & & & 0.95 \\ \hline
**Proposed Method** & **Imagenet + Kinetics** & **UFLV-DS** & **RGB, OF** & **2-Stream 3D-CNN** & **0.97** & **0.97** & **0.98** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison with the state-of-the-art for violence.  
dataset have shown that the approach can detect falling, loitering, and violence with an accuracy of up to 99%, 97%, and 98%, respectively. Similarly, behavior classification performs well in terms of precision and recall as well. Precisely, it achieves \(F_{1}\) scores of 98%, 93%, and 97% for falling, loitering, and violence detection.  
In future research, the author intends to investigate the inclusion of more abnormal behaviors (e.g., person in the wrong place) as well as the possibility of training deeper convolutional layers on other datasets to improve the model performance. Similarly, the combined detection of multiple behaviors in a single scene (e.g., loitering may take place while someone is falling) is also an interesting direction for future research. Furthermore, although optical flow is a powerful technique to represent motion, it also results in expensive computations. Therefore, to avoid any preprocessing steps, the design of more complex architectures may be investigated that can learn motion representations from raw data.  
The author acknowledges the Deanship of Scientific Research at King Faisal University for the financial support under Nahser Track (Grant No. 186330).  
Not applicable.  
The datasets used in this study are publicly available from sources cited in the paper.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.