# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 4 Results
### Classification Results
AKAZE consistently outperforms FAST and SIFT, demonstrating superior accuracy and AUC scores. This observation suggests that AKAZE possesses inherent capabilities that enable it to effectively capture the distinctive characteristics of the crowd anomalies within the violence in crowds dataset. Shifting our focus to the neural network configurations, we observe that the PCA/NN configuration consistently outperforms both the NN and 128/NN configurations in terms of accuracy and AUC scores. This outcome highlights the significance of employing dimensionality reduction techniques, specifically through principal component analysis (PCA), to enhance the performance of crowd anomaly detection on the violence in crowds dataset. By reducing the dimensionality of the data, PCA facilitates the extraction of salient features, thereby leading to improved accuracy and AUC scores. The obtained results for the violence in crowds dataset emphasise the importance of carefully selecting and evaluating the appropriate combination of feature point extraction methods and neural network configurations for each dataset. It is evident that the performance of these methods can be subject to variations based on the dataset's characteristics and the specific nature of crowd anomalies. Therefore, it is important to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scene 1** & **NN** & **128/NN** & **PCA/NN** \\ \hline \multirow{3}{*}{SIFT} & ACC: 0.995 & ACC: 0.985 & ACC: 0.986 \\  & AUC: **0.990** & AUC: 0.979 & AUC: 0.983 \\  & ACC: 0.988 & ACC: 0.986 & ACC: 0.988 \\  & AUC: 0.977 & AUC: 0.973 & AUC: 0.978 \\  & ACC: 0.953 & ACC: 0.952 & ACC: 0.966 \\  & AUC: 0.904 & AUC: 0.943 & AUC: 0.955 \\ \hline
**Scene 2** & **NN** & **128/NN** & **PCA/NN** \\ \hline \multirow{3}{*}{FAST} & ACC: 0.936 & ACC: 0.926 & ACC: 0.924 \\  & AUC: 0.901 & AUC: 0.893 & AUC: 0.885 \\  & ACC: 0.954 & ACC: **0.961** & ACC: 0.951 \\  & AUC: 0.937 & AUC: **0.943** & AUC: 0.931 \\  & ACC: 0.937 & ACC: 0.918 & ACC: 0.917 \\  & AUC: 0.892 & AUC: 0.892 & AUC: 0.872 \\ \hline
**Scene 3** & **NN** & **128/NN** & **PCA/NN** \\ \hline \multirow{3}{*}{SIFT} & ACC: **0.990** & ACC: 0.983 & ACC: 0.985 \\  & AUC: **0.976** & AUC: 0.980 & AUC: 0.968 \\ \cline{1-1}  & ACC: 0.981 & ACC: 0.983 & ACC: 0.983 \\ \cline{1-1}  & AUC: 0.956 & AUC: 0.973 & AUC: 0.959 \\ \cline{1-1}  & ACC: 0.975 & ACC: **0.990** & ACC: 0.985 \\ \cline{1-1}  & AUC: 0.914 & AUC: **0.988** & AUC: 0.968 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results for the UMN dataset (Scene 1, Scene 2, and Scene 3) with descriptors and methods. Best accuracy is given in bold. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **NN** & **128/NN** & **PCA/NN** \\ \hline \multirow{3}{*}{SIFT} & ACC: 0.803 & ACC: 0.799 & ACC: 0.848 \\  & AUC: 0.805 & AUC: 0.804 & AUC: 0.846 \\ \cline{1-1}  & ACC: 0.848 & ACC: 0.844 & ACC: 0.844 \\ \cline{1-1}  & AUC: 0.851 & AUC: 0.847 & AUC: 0.851 \\ \cline{1-1}  & ACC: 0.856 & ACC: **0.885** & ACC: 0.873 \\ \cline{1-1}  & AUC: 0.862 & AUC: **0.895** & AUC: 0.878 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification results for the violence in crowds dataset (with descriptors and methods). Best accuracy is given in bold. exercise caution and conduct thorough evaluations to identify the optimal combination of methods that will yield the highest performance in crowd anomaly detection tasks. Figure 3 encapsulates the results derived from the application of three classification approaches used in this work on the UMN and violence in crowds datasets, using the SIFT, FAST, and AKAZE descriptors. The performance of the crowd anomaly detection methods is generally higher on the UMN dataset compared to the violence in crowds dataset. The UMN dataset provides well-defined scenarios, while the violence in crowds dataset has more diverse and challenging real-world conditions. The controlled settings in the UMN dataset and the more precise differentiation between normal and abnormal behaviours contribute to the higher performance.