## 4 Experimentation
### Experiment Results and Ablation Study
#### 4.4.4 Anomalous Event Recognition and Localization of Moving Objects  
Figure 8 demonstrates the anomalous event recognition task along with the localization of moving objects. Tables 4 and 6 show the effectiveness of the 3D ResNet [21] used in our study. Table 6 specifically shows its near-similar accuracy on the activity recognition task compared to the work of [57], which uses a heavier network than 3D ResNet. Therefore, we have utilized 3D ResNet for extracting useful features from videos, and its  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segments** & **8** & **16** & **32** & **48** & **64** \\ \hline
**UCF-Crime** (**AUC**) & - & 81.91 & 81.17 & 80.71 & 80.98 \\ \hline
**ShanghaiTech** (**AUC**) & 68.20 & 68.50 & 65.60 & 66.20 & 66.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of choosing a different number of segments on UCF-Crime and ShanghaiTech datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ranking Measures** & **UCF-Crime** & **ShanghaiTech** \\ \hline
3D ResNet-34 + \(\text{rm}_{1}\) & 76.20 & 61.20 \\
3D ResNet-34 + \(\text{rm}_{1}\) + MF & 78.68 & 65.30 \\
3D ResNet-34 + \(\text{rm}_{1}\) + MF + \(\text{rm}_{2}\) & 78.60 & 64.90 \\
3D ResNet-34 + \(\text{rm}_{1}\) + MF + \(\text{rm}_{3}\) & 80.08 & 66.10 \\
3D ResNet-34 + \(\text{rm}_{1}\) + MF + \(\text{rm}_{4}\) & 80.01 & 65.60 \\
3D ResNet-34 + \(\text{rm}_{1}\) + MF + \(\text{rm}_{2}\) +\(\text{rm}_{3}\) & 80.92 & 67.70 \\
3D ResNet-34 + \(\text{rm}_{1}\) + MF + \(\text{rm}_{2}\) +\(\text{rm}_{4}\) & 79.80 & 65.40 \\
3D ResNet-34 + \(\text{rm}_{1}\) + MF + \(\text{rm}_{2}\) +\(\text{rm}_{3}\) + \(\text{rm}_{4}\) & 80.10 & 67.00 \\
3D ResNet-34 + \(\text{MF}\) + MRM & 81.12 & 67.91 \\ \hline \hline
**DMRMs** 3D ResNet-34 + MF + MRM + \(\epsilon_{1}+\epsilon_{2}\) & **81.91** & **68.50** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effectiveness of proposed ranking measures \(\text{rm}_{1}\), \(\text{rm}_{2}\), \(\text{rm}_{3}\), \(\text{rm}_{4}\), MRMs and constraints (sparsity and temporal smoothness) as described in Equations (9)-(15), using 3D ResNet-34 [21] and using motion flow maps (MFM) is shown with frame-level AUC-Comparison (in %) on UCF-Crime and ShanghaiTech datasets. It shows that collectively using 3D ResNet, motion flow and MRMs in our proposed framework contributes to outperforming state-of-the-art methods. Bold figures stand for the best performance in all.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **[PERSON] et al. [13]** & **[PERSON] et al. [8]** & **[PERSON] et al. [14]** & **Ours DMRMs** \\ \hline FAR & 27.20 & 3.10 & 1.90 & **0.85** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of false alarm rate (FAR) on UCF-Crime [14] test dataset in normal event videos. Bold figure stands for the best performance in all.  
effectiveness is shown in Table 4. Table 6 also demonstrates that the UCF-Crime dataset is a challenging dataset not only in the anomalous event detection task but also in the activity recognition task. Figure 8 shows the success and failure of both cases of the anomalous event recognition task.