## 4 Experimentation
### Experiment Results and Ablation Study  
#### 4.4.1 Comparison among State-of-the-Art Methods  
The UCF-Crime and ShanghaiTech datasets are the newest datasets; therefore, only recent algorithms [5; 8; 9; 11; 13; 14; 44; 56; 5] have reported their results on these datasets. In the above-mentioned algorithms, the training phase is different from our framework, DMRMs. Unlike ours, most of them have used only normal videos to train their deep neural networks. However, we have used the same UCF-Crime and ShanghaiTech testing datasets for fair comparison among all algorithms. This study compares our proposed anomalous event detection framework, DMRMs, with other state-of-the-art methods, [PERSON] et al. [13], [PERSON] et al. [44], [PERSON] et al. [8], GODS [56], [PERSON] et al. [14], and an SVM binary classifier using ROC-Curve and AUC metric on the UCF-Crime dataset. As shown in Figure 3, our proposed framework, DMRMs, outperforms all state-of-the-art methods, including the recent algorithm by [PERSON] et al. [14], which was the primary motivation for the present work, and gives the best FPR-TPR trade-off curve among them. Similarly, from Table 2, the highest AUC score reflects the effectiveness of our proposed framework. Moreover, AUC mentioned by authors [PERSON] et al. [44] in Table 2 was reported in the paper GODS [56]. Similarly, the performance of our proposed framework, DMRMs, achieves competitive results on the ShanghaiTech dataset using the AUC metric evaluation method when compared with the state-of-the-art methods, as shown in Table 2. Here, we have evaluated the AUC of each previously proposed algorithms [5, 9, 11, 13, 45] using the same experimental setup on the testing dataset as ours. The AUC metric is calculated on the overall ShanghaiTech testing dataset (\(107\) testing videos) in the [PERSON] et al. [13]* algorithm. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Hyper-Parameters** & \(\mathbf{\gamma_{1}}\) & \(\mathbf{\gamma_{2}}\) & \(\mathbf{73}\) & \(\mathbf{74}\) & **lr** & **UCF-Crime** & **ShanghaiTech** \\ \hline
**1** & \(8\times 10^{-5}\) & \(8\times 10^{-5}\) & \(8\times 10^{-5}\) & 0.01 & 0.001 & 81.21 & 65.47 \\
**2** & \(8\times 10^{-5}\) & \(8\times 10^{-5}\)  
Table 2 shows that our framework, DMRMs, performs quite well on the UCF-Crime dataset compared to the ShanghaiTech dataset. There are mainly two reasons that our framework, DMRMs, does not function well on the ShanghaiTech dataset. First, our model requires a large amount of dataset to train the proposed architecture as it utilizes 3D ResNet-34 to extract deep features from the video. As 3D ResNet is not explored adequately in the anomalous activity detection task, consequently, according to the action recognition task's studies [22; 23], similar to our task, 3D ResNet overfits on the smaller dataset. Moreover, compared to the ShanghaiTech dataset UCF-Crime dataset is vast. Second, the ShanghaiTech dataset has much noisier annotations compare to the UCF-Crime dataset, and this dataset includes videos with a lot of illumination and camera angle variations. Recently, the authors [18] have utilized the graphical noise cleaner approach resulting in better performance than our method. However, our study does not include noise cleaner. Therefore, there is an enormous variation in our algorithm's performance \begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **UCF-Crime** & **ShanghaiTech** \\ \hline SVM Binary Classifier & 50.00 & - \\ [PERSON]. [PERSON] et al. [13] & 50.60 & 60.90 * \\ [PERSON]. [PERSON] et al. [44] & 58.50 & - \\ [PERSON] et al. [8] & 65.51 & - \\ [PERSON] et al. [5] & - & 60.73 \\ GODS [56] & 70.46 & - \\ Autoreg-ConvAE (LLK) [45] & - & 70.30 \\ [PERSON] et al. [9] & - & 73.04 \\ Autoreg-ConvAE (NS) [45] & - & 73.85 \\ MPED-RNN [11] & - & **76.24** \\ [PERSON] et al. [14] & 75.41 & - \\ \hline
**Ours DMRMs** & **81.91** & 68.50 \\ \hline \hline \end{tabular}
\end{table}