# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 5 Conclusions and Future Study  
This study presents a pioneering approach to crowd anomaly detection, capitalising on the fusion of visual descriptors and neural networks. The method's efficacy, showcased through rigorous experiments on the established UMN and violence in crowds datasets, underscores its potential in accurately identifying aberrant crowd behaviours. The superiority of the SIFT and AKAZE descriptors, combined with the competitive performance of the neural network configurations, highlights the robustness of our approach. As such, this research contributes significantly to the field's evolving landscape, providing a strong foundation for the future development of crowd anomaly detection systems.  
As the research progresses, several promising avenues for further exploration in the field have been identified:  
1. Transitioning from conventional neural networks to advanced deep learning architectures promises enhanced performance;
2. Incorporating cutting-edge feature extraction methods can provide more comprehensive insights into crowd behaviour patterns;
3. To enable real-world applicability, rigorous testing on real-time datasets that encompass distortions and complexities is necessary;
4. The fusion of multimodal data and the extension of the methodology to detect various types of anomalies hold substantial potential.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **AUC \%** & **Accuracy \%** \\ \hline Optical Flow [16] & 84.0 & / \\ SFM [16] & 96.0 & / \\ Sparse Reconstruction [43] & 97.0 & / \\ Visual Descriptors [37] & Scene 1: 0.98.72, Scene 2: 95.21, & / \\  & Scene 3: 97.50 & / \\ Optical Flow, GAN [27] & 98.1 & / \\ Our Approach & Scene 1: 99.0, Scene 2: 94.3, & Scene 1: 99.5, Scene 2: 96.1, \\  & Scene 3: 98.8 & Scene 3: 99.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison of the results on the UMN dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **AUC \%** & **Accuracy \%** \\ \hline VIF [42] & 85 & 81.30 \\ Visual Descriptors [37] & 88 & 84.44 \\ Our Approach & 89.5 & 88.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison of the results on the violence in crowds dataset.  
We believe that these future directions will collectively pave the way for refined, adaptable, and multifaceted crowd anomaly detection systems.  
Conceptualization and methodology, S.A. and S.L.; software, S.A.; Formal analysis, S.A. and S.L.; Writing--original draft preparation, S.A.; Writing--review and editing, S.A., S.L., P.G. and S.C.; Supervision, S.L., P.G. and S.C.; Project administration, S.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://mha.cs.umn.edu/proj_events.shtml](https://mha.cs.umn.edu/proj_events.shtml)]([https://mha.cs.umn.edu/proj_events.shtml](https://mha.cs.umn.edu/proj_events.shtml)).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CAD & Crowd Anomaly Detection \\ PCA & Principle Component Analysis \\ FAST & Features from the Accelerated Segment Test \\ SIFT & Scale-Invariant Feature Transform \\ AKAzE & Accelerated-KAZE \\ NN & Neural Network \\ ACC & Accuracy \\ AUC & Area Under the Curve \\ \end{tabular}