# DualCascadeTSF-MobileNetV2: A Lightweight Violence Behavior Recognition Model
## 5 Discussion  
As can be seen in Table 3, the DualCascadeTSF-MobileNetV2 model proposed in this paper has achieved results that are close to or even better than those of other classic models in the three violent behavior datasets. On the Crowd Violence dataset, the accuracy of DualCascadeTSF-MobileNetV2 reaches 98.98%, which is higher than other classical models and represents an improvement of 0.83% compared to the previously best-performing model, SAM. On the RWF-2000 dataset, its accuracy was 88.5%, higher than eight models and slightly lower than four models, with only a 2.5% gap compared to the highest-accuracy model, P-TSM. On the Hockey Fights dataset, its accuracy was 98.0%, higher than nine models and slightly lower than four models, with only a 1.1% gap compared \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Parameters (MB)** & **Training Speed (min)** & **Memory Size (MB)** & **GFLOPs** \\ \hline
3D-CNN [31] & 297.83 & 248.38 & 2647.7 & 191.6 \\ LRCN [32] & 237.83 & 185.12 & 1212.93 & 186.0 \\ I3D [33] & 46.88 & 123.8 & 1000.2 & 35.6 \\ TSN [40] & 89.69 & 46.68 & 390.05 & 125.3 \\ TSM [1] & 89.69 & 113.43 & 397.71 & 68.1 \\ TEA [36] & 91.95 & 190.8 & 479.78 & 69.9 \\ Two-cascade TSM [15] & 89.69 & 128.33 & 397.71 & 68.1 \\ P-TSM [39] & 89.69 & 87.63 & 396.57 & 68.1 \\ MobileNet-TSM [29] & 8.49 & 32.68 & 175.86 & 5.0 \\ DualCascadeTSM-MobileNetV2 (ours) & 16.99 & 35.92 & 347.13 & 5.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the number of parameters, memory size, and training speed between DualCascadeTSF-MobileNetV2 and other classic models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Crowd Violence (\%)** & **RWF-2000 (\%)** & **Hockey Fights (\%)** \\ \hline ResNet-50 [30] & 93.878 & 84 & 95.5 \\
3D-CNN [31] & 94.3 & 82.75 & 94.4 \\ LRCN [32] & 94.57 & 77 & 97.1 \\ I3D [33] & 88.89 & 85.75 & 97.5 \\ MiNet-3D [34] & 91.41 & 81.98 & 94.71 \\ AR-Net [35] & 95.918 & 87.3 & 97.2 \\ TSM [1] & 95.95 & 88 & 97.5 \\ TEA [36] & 96.939 & 88.5 & 97.7 \\ Two-cascade TSM [15] & 96.939 & 89 & 98.05 \\ SAM [37] & 98.15 & 89.1 & **99.1** \\ SSHA [38] & - & 90.4 & 98.7 \\ P-TSM [39] & 96.969 & **91** & 98.5 \\ MobileNet-TSM [29] & 97.959 & 87.75 & 97.5 \\ DualCascadeTSM-MobileNetV2 (ours) & **98.98** & 88.5 & 98.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of accuracy rates of DualCascadeTSF-MobileNetV2 with other classical models on the three datasets. to the highest-accuracy model, SAM. Compared to MobileNet-TSM, which also uses MobileNetV2 as the base model, the accuracy of the model in this paper is 1.021%, 0.75%, and 0.5% higher on the three datasets, respectively. Compared with the lightweight model MiNet-3D, the accuracy of the model in this paper is 7.57%, 6.52%, and 3.29% higher on the three datasets, respectively. Overall, although there is still a certain gap between DualCascadeTSF-MobileNetV2 and the most advanced algorithms in terms of accuracy, its performance is better than that of most models, especially among lightweight models, and it is significantly better than MobileNet-TSM and MiNet-3D. Moreover, it can be seen from Table 4 that in terms of the number of parameters, DualCascadeTSF-MobileNetV2 has a parameter size of 16.99 MB. In comparative experiments, although this value is slightly higher than that of MobileNet-TSM, it still significantly reduces the parameter scale compared to other classic models. This indicates that DualCascadeTSF-MobileNetV2 has adopted effective strategies in its design to reduce redundant parameters while ensuring that the model can capture key features in videos. Secondly, regarding memory usage, DualCascadeTSF-MobileNetV2 occupies a memory size of 347.13 MB. Although slightly larger than MobileNet-TSM, considering its accuracy rate of 98.98% on the Crowd Violence dataset and its equally outstanding performance on the RWF-2000 and Hockey Fights datasets, this moderate increase in memory consumption is acceptable.