# Temporal Saliency-Based Suspicious Behavior Pattern Detection
## 3 Results and Discussion
### Comparison Results from Experiments with UMN Dataset and Avenue Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**UMN Dataset** & **Proposed Method** & **[8]** & **[25]** & **[11]** & **[10]** & **[12]** \\ \hline Scene 1: Lawn & 99.20\% & 99.10\% & 99.03\% & 90.62\% & 84.41\% & 90.52\% \\ Scene 2: Indoor & 97.10\% & 94.85\% & 95.36\% & 85.06\% & 82.35\% & 78.48\% \\ Scene 3: Plaza & 93.20\% & 97.76\% & 96.63\% & 91.58\% & 90.83\% & 92.70\% \\ \hline average & 96.50\% & 96.46\% & 96.40\% & 87.91\% & 85.09\% & 84.70\% \\ \hline \end{tabular}
\end{table} 
Table 2: Accuracy comparison with state-of-the art methods on the UMN dataset. Figure 8: Examples of detection results for abnormality from No. 1 data (UMN Scene 1: Lawn). Due to the unpredictability of abnormal events, most previous approaches employ a learning process, and most of them only learn normal event models in an unsupervised or semi-supervised manner, and abnormal events are considered to be patterns that significantly deviate from the created normal event models [29]. The method used in [21] uses spatio-temporal convolutional neural networks to extract and learn various features, and the method in [29] employs the online dictionary \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Dataset** & **Accuracy** & **Precision** & **Recall** \\ \hline
1 & UMN dataset: Lawn & 99.2\% & 99.8\% & 91.1\% \\
2 & UMN dataset: Lobby & 97.1\% & 99.5\% & 93.7\% \\
3 & UMN dataset: Park & 93.2\% & 98.9\% & 92.5\% \\
4 & Avenue dataset & 90.1\% & 93.2\% & 94.5\% \\
5 & Walk dataset & 100\% & 100\% & 100\% \\
6 & Bump data & 95.8\% & 100\% & 94.4\% \\
7 & Fall down data & 95\% & 91.6\% & 100\% \\
8 & Water data & 82.7\% & 79.1\% & 100\% \\
9 & Stairs fall down data & 94.4\% & 100\% & 84.6\% \\
10 & CCTV violent robbery & 91.4\% & 100\% & 90.6\% \\  & data in South Kensington & & & \\ \hline  & average & 93.89\% & 96.21\% & 94.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The overall performance evaluation result of the proposed method. Figure 10: Examples of detection results for abnormality from No. 3 data (UMN Scene 3: Plaza). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Proposed Method** & [26] & [29] & [21] \\ \hline Avenue dataset & 90.18\% & 87.70\% & 87.19\% & 80.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: AUC (area under the curve) comparison with state-of-the art methods on the Avenue dataset. Figure 9: Examples of detection results for abnormality from No. 2 data (UMN Scene 2: Indoor). learning and sparse reconstruction framework. The method in [26] used both training data and testing data to make a global grid motion template (GGMT). As mentioned before, even though the proposed method did not employ any learning process and uses simple motion features, the proposed method outperforms the comparison methods. Figure 11 demonstrates the examples of detection results for abnormality from No. 4, which were chosen from the Avenue dataset in the proposed method. In this video sequence, most people in front of the building are walking to the right, while a child jumps to the left. The proposed method properly detected the area where the child jumps. We can see that the percentage of the child occupying the image has increased, because the distance between the child and the camera is much closer than that of the other people. Due to this, the average direction was calculated as the left direction, from the moment the child jumps. Since the child is moving to the left during the jump, the whole direction was calculated correctly. It is seen that different types of abnormality such as running and jumping can be accurately detected and localized. Analysis of Examples of Detection Results for Abnormalities with 10 Different Types of Video Sequences  
Figures 8-17  
Figure 16: Examples of detection results for abnormality from the No. 9 data. Figure 14: Examples of detection results for abnormality from the No. 7 data. Figure 13: Examples of detection results for abnormality from the No. 6 data. Figure 15: Examples of detection results for abnormality from the No. 8 data.