# DualCascadeTSF-MobileNetV2: A Lightweight Violence Behavior Recognition Model
## 3 Methods
### DualCascadeTSF-MobileNetV2
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Weights** & **Computational Complexity** & **Top-1 Acc.** & **Top-5 Acc.** & **Dataset** \\ \hline SqueezeNet\_8 bit [9] & 0.66 & — & 57.5 & 80.3 & ImageNet \\
1.0\_SqueezeNext\_23 [16] & 0.72 & 282M MAC & 58.98 & 82.33 & ImageNet \\ ShiftNet\_A [17] & 4.1 & — & 70.1 & — & ImageNet \\ MobileNet\_1 [6] & 4.2 & 569M Mult-Add & 70.6 & — & ImageNet \\ MobileNetV2\_1 [7] & 3.4 & 300M Mult-Add & 72.0 & — & ImageNet \\ MobileNet\_3\_Large\_1 [10] & 4.0 & 155M Mult-Add & 73.3 & — & ImageNet \\ MobileNetV4\_Conv\_M [18] & 9.2 & 1.0G MAC & 79.9 & — & ImageNet \\ EfficientNet\_B0 [11] & 5.3 & 0.39B FLOP & 77.1 & 93.3 & ImageNet \\ EfficientNetV2\_M [19] & 54 & 24B FLOP & 85.1 & — & ImageNet \\ RepViT\_M1.0 [20] & 6.8 & 1.1G MAC & 80.0 & — & ImageNet \\ GhostNet\_1 [21] & 5.2 & 141M FLOP & 73.9 & 91.4 & ImageNet \\ GhostNetV2\_10 [22] & 6.1 & 167M FLOP & 75.3 & 92.4 & ImageNet \\ C\_GhostNet\_1 [23] & 5.2 & 141M FLOP & 73.9 & 91.4 & ImageNet \\ G\_GhostNet\_w/\_mix [23] & 14.6 & 2.3B FLOP & 73.1 & 91.2 & ImageNet \\ ShuffleNet\_1(g=8) [8] & — & 140M FLOP & 67.6 & — & ImageNet \\ ShuffleNetV2\_1 [24] & — & 146M FLOP & 69.4 & — & ImageNet \\ EffNet [25] & 0.14 & 11.4M FLOP & 80.20 & — & CIFAR-10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the performance of classic convolutional structure models. The models we list are all versions without expansion or compression. In SqueezeNet_8 bit, 8 bit” refers to the data type. “1.0\_SqueezeNext_23” indicates a version without expansion and composed of 23 modules. “ShiftNet_A” denotes its Type A. “MobileNetV4_Conv-M” represents the convolutional version and is the medium-sized model. “EfficientNet_B0” is the smallest base model, while “EfficientNetV2_M” is the medium-sized version. “RepViT_M1.0” indicates the medium-sized version without expansion, and “G_GhostNet_w/_mix” refers to the version with mix operations. “ShuffleNet_1(g = 8)” means that the channels are divided into eight groups. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Structure** & **DualCascadeTSF Included** & **Output Size** & **Repetition Times** \\ \hline conv1 & \(3\times 3\times 32\) & & \(112\times 112\) & \(\times 1\) \\ bottleneck1 & \(1\times 1\times 32\), \(3\times 3\times 32\), \(1\times 1\times 16\) & Yes & \(112\times 112\) & \(\times 1\) \\ bottleneck2 & \(1\times 1\times 96\), \(3\times 3\times 96\), \(1\times 1\times 24\) & Yes & \(56\times 56\) & \(\times 2\) \\ bottleneck3 & \(1\times 1\times 192\), \(3\times 3\times 192\), \(1\times 1\times 32\) & Yes & \(28\times 28\) & \(\times 3\) \\ bottleneck4 & \(1\times 1\times 384\), \(3\times 3\times 384\), \(1\times 1\times 64\) & Yes & \(14\times 14\) & \(\times 4\) \\ bottleneck5 & \(1\times 1\times 576\), \(3\times 3\times 576\), \(1\times 1\times 96\) & Yes & \(14\times 14\) & \(\times 3\) \\ bottleneck6 & \(1\times 1\times 960\), \(3\times 3\times 960\), \(1\times 1\times 160\) & Yes & \(7\times 7\) & \(\times 3\) \\ bottleneck7 & \(1\times 1\times 960\), \(3\times 3\times 960\), \(1\times 1\times 320\) & Yes & \(7\times 7\) & \(\times 1\) \\ conv2 & \(1\times 1\times 1280\) & & \(7\times 7\) & \(\times 1\) \\ Avgpool & Pool \(7\times 7\) & & \(1\times 1\) & \(\times 1\) \\ FC & Linear \(1280\to 2\) & & \(1\times 1\) & \(\times 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structure of DualCascadeTSF-MobileNetV2. features to more complex patterns, which helps to capture high-level features in the input data and makes it more suitable for handling complex video analysis tasks. This strategy effectively improves the efficiency and effectiveness of the model and achieves a good balance between performance and resource utilization. The bottleneck layer consists of a pointwise convolution for channel expansion, a depthwise convolution for feature extraction, and another pointwise convolution for channel compression.