## 4 Experiments
### Results
#### 4.3.4 Multi-Source Feature Fusion Inference Results  
To validate the effectiveness of the proposed multi-source feature fusion inference method, a comparison is conducted between the pre-optimization and post-optimization decision-making processes. Prior to decision optimization, the output of the method consists of normalized confidence scores for behaviors, which are not directly suitable for anomaly detection. Consequently, a decision function must be applied to transform these confidence scores into prediction results. \[D_{pre}= \begin{cases}\textit{smoking}&\begin{subarray}{c}IF\ C_{b}(smoking)> \varphi_{pre}\\ \wedge C_{b}(smoking)>C_{b}(\textit{playing on a phone})\\ \textit{playing on a phone}&\begin{subarray}{c}IF\ C_{b}(playing on a phone)> \varphi_{pre}\\ \wedge C_{b}(\textit{playing on a phone})>C_{b}(\textit{smoking})\end{subarray} \end{cases} \end{cases} \tag{10}\]  
where \(D_{pre}\) represents the anomaly behavior classification without employing multi-source feature fusion. The optimal parameter \(\varphi_{pre}=0.65\) is determined through the use of a grid search method. In contrast, the method utilizing multi-source feature fusion initially decouples the driving state and, subsequently, evaluates it using the AFAR. The test data  
Figure 7: Comparison of video inference results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Behavior** & \begin{tabular}{c} **SlowFast-** \\ **ResNet50 (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **TSM–ResNet50** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Improved TSM (\%)** \\ \end{tabular} \\ \hline \hline Sitting & 83.24 & 84.53 & 89.27 \\ Standing & 82.45 & 85.08 & 88.63 \\ Walking & 81.03 & 83.47 & 87.15 \\ Lying & 84.12 & 85.62 & 88.29 \\ Driving & 80.58 & 82.14 & 91.35 \\ Playing on a phone & 58.31 & 62.08 & 65.42 \\ Smoking & 60.17 & 64.25 & 68.09 \\ Top-1 accuracy & 75.10 & 77.79 & 82.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of class-wise accuracy for different models. are derived from the SCA dataset, comprising a total of 954 samples, including 306 anomaly samples. Among these anomaly samples, 184 instances involve a person smoking and 122 instances involve a person playing on a phone. The relevant evaluation metrics and detailed test data are summarized in Table 10.  
To evaluate the reasoning effectiveness of the optimized decision-making method, two surveillance video segments featuring a person smoking and a person playing on a phone are employed for testing. As illustrated in Figure 8a, a driver is observed holding a cigarette. However, due to the absence of critical smoking action features, the action score remains low. Consequently, after feature fusion, the behavior is correctly classified as normal. In Figure 8b, a driver exhibits key action characteristics indicative of smoking, which increases the action score, thereby elevating the overall behavioral score beyond the predefined threshold, resulting in successful anomaly detection. Moreover, fundamental mutually exclusive behaviors, such as sitting and non-driving, are accurately identified. Prior to its application, the lack of auxiliary judgment based on object features often leads to inaccurate behavior categorization by the model. Figure 8c depicts a typical ship's bridge scene filled with electronic instruments, where a driver is partially obscured by another passing individual, while positioned at the control panel. The proposed feature fusion approach accurately identifies both the driver and the mobile phone by considering the driver's actions and their association with object features comprehensively. Consequently, the driver's behavior is successfully categorized as standing, distracted driving (playing on a phone). Furthermore, a surveillance video containing multiple drivers is selected for analysis to evaluate the effectiveness of the proposed method in crowded scenarios.