# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 4 Results
### Classification Results  
The following tables display the accuracy and area under the curve (AUC) scores obtained for each combination of feature point extraction methods (SIFT, FAST, AKAZE) applied to the two datasets utilised in this analysis. The results are presented for three different configurations of the neural network: NN, 128/NN, and PCA/NN. The NN configuration uses the neural network without any dimensionality reduction. In the 128/NN configuration, dimensionality reduction is performed using an autoencoder to reduce the feature space to 128 dimensions. Lastly, in the PCA/NN configuration, dimensionality reduction is achieved by applying principal component analysis (PCA) with a variance threshold of 95. The results obtained for the UMN dataset and the violence in crowds dataset are presented in Tables 2 and 3, respectively. Five-fold cross-validation is used in the classification. There are a total of five tests taken, with four training sets used for each. The average prediction accuracy (ACC) and the area under the curve (AUC) are reported for the results. The results of Scene 1 of the UMN dataset show that all three feature point extraction methods (SIFT, FAST, and AKAZE) achieve high accuracy and AUC scores across different neural network configurations. The NN configuration generally performs slightly better than the 128/NN and PCA/NN configurations. Among the feature point extraction methods, SIFT consistently exhibits the highest accuracy and AUC scores, followed closely by FAST and AKAZE. This indicates that SIFT is particularly effective in capturing spatial information and detecting anomalies in crowd behaviour for the Scene 1 dataset. The results on Scene 2 of the UMN dataset show comparable performance among the feature point extraction methods and neural network configurations. SIFT consistently achieves the highest accuracy and AUC scores, followed by FAST and AKAZE. However, the overall accuracy and AUC scores are slightly lower compared to Scene 1. This suggests that Scene 2 may pose more significant challenges in crowd anomaly detection, requiring more robust feature extraction methods and network configurations. The results for Scene 3 indicate consistent performance across the feature point extraction methods and neural network configurations. AKAZE demonstrates the highest accuracy and AUC scores, followed by SIFT and FAST. The overall performance in Scene 3 is similar to that in Scene 1, with slightly lower scores. This suggests that the crowd behaviour in Scene 3 may exhibit similar characteristics to that in Scene 1 but with some variations. The results reveal that SIFT consistently outperforms FAST and AKAZE in terms of accuracy and AUC scores across all scenes. Notably, AKAZE shows promise, particularly in Scene 3. These findings highlight the robustness of SIFT and AKAZE as feature point extraction methods for crowd anomaly detection in various scenes. In terms of the neural network configurations, the NN configuration consistently achieves the highest accuracy and AUC scores, followed by the 128/NN and PCA/NN configurations. This suggests that employing a deeper neural network without dimensionality reduction captures more intricate patterns in crowd behaviour. However, the differences between the configurations are relatively minor, indicating that dimensionality reduction techniques such as 128/NN and PCA/NN can yield competitive results with lower computational complexity. Overall, combining SIFT with the NN configuration emerges as a particularly effective approach for crowd anomaly detection, delivering superior performance and enabling the identification of complex crowd behaviours. The classification results for the violence in crowds dataset, as presented in Table 2, exhibit some variations compared to the UMN dataset. In the case of the violence in crowds dataset, the performance of all three feature point extraction methods, namely SIFT, FAST, and AKAZE, shows slightly lower accuracy and AUC scores compared to the UMN dataset.