## 3 The Proposed Approach
### Multi-Source Feature Fusion Based on RS
Due to the significant similarities in motion characteristics between smoking and playing on a phone, such as the interaction of specific objects with the person's head and the pronounced arm movements involved in both answering a phone call and bringing a cigarette to the person's mouth. The TSM model, despite its efficacy in extracting temporal features from adjacent frames, encounters challenges in accurately distinguishing the subtle local differences between these two behaviors. This limitation often results in confusion, suggesting a potential action association between smoking and playing on a phone during the behavior classification process. To address this issue, the confidence levels of the two behaviors are proportionally integrated to generate an action score. This action score is subsequently combined with the object feature score to compute the final abnormal behavior score for classification purposes. The decision rule set is as follows: \begin{table}
\begin{tabular}{c c c} \hline \hline  & Confidence of Associated Feature & \(C_{o}(cigarette)\in[0,1]\) \\  & Objects & \(C_{o}(phone)\in[0,1]\) \\ \hline  & & \(C_{b}(driving\ )\in[0,1]\) \\  & & \(C_{b}(smoking)\in[0,1]\) \\ Condition Attribute & Behavior Confidence & \(C_{b}(sitting)\in[0,1]\) \\  & & \(C_{b}(standing)\in[0,1]\) \\  & & \(C_{b}(walking)\in[0,1]\) \\  & & \(C_{b}(lying)\in[0,1]\) \\ \cline{2-3}  & Coverage Rate of & \(S_{o}(cigarette)\in[0,1]\) \\  & Anomalous Features & \(S_{o}(\ phone)\in[0,1]\) \\ \hline Decision & & \(D_{a}\in\)_[non-driving (smoking), non-driving (playing on a phone), distracted driving (smoking), distracted driving (playing on a phone))_ \\ Attribute & & \(D_{b}\in\{sitting,\ standing,\ walking,\ lying\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The decision table. \[D_{a}=\left\{ \begin{array}{ll}non-driving\ (playing\ on\ a\ phone)&IF\ C_{b}(driving\ )<\tau\wedge M(playing\ on\ a\ phone)>\varphi\\ non-driving\ (smoking)&IF\ C_{b}(driving\ )<\tau\wedge M(smoking)>\varphi\\ distracted\ driving\ (smoking)&IF\ C_{b}(driving\ )<\tau\wedge M(smoking)>\varphi\\ distracted\ driving\ (playing\ on\ a\ phone)&IF\ C_{b}(driving\ )<\tau\wedge M(playing\ on\ a\ phone)>\varphi\\ \end{array} \right. \tag{4}\]  
where \(\varphi\) denotes the threshold score for identifying abnormal behaviors, while \(\tau\) represents the threshold score for assessing the driving state. Specifically, \(M(smoking)\) and \(M(playing\ on\ a\ phone) refer to the scores for abnormal behaviors corresponding to smoking and playing on a phone, respectively, which are defined as follows:  
\[M(smoking)=\alpha\cdot S_{o}(cigarette)\cdot C_{o}(cigarette)+\beta\cdot C_{b}( smoking)+\gamma\cdot C_{b}(playing\ on\ a\ phone) \tag{5}\]  
\[M(playing\ on\ a\ phone)=\alpha\cdot S_{o}(phone)\cdot C_{o}(phone)+\beta\cdot C_{b }(playing\ on\ a\ phone)+\gamma\cdot C_{b}(smoking) \tag{6}\]  
where \(\alpha\) denotes the weight coefficient associated with the feature score, whereas \(\beta\) and \(\gamma\) represent the weight coefficients corresponding to the direct and indirect action scores, respectively. Utilizing the grid search method within the SCA dataset, with the Comprehensive Classification Accuracy (CCA) as the evaluation metric, and setting the search range to [0, 1], with a step size of 0.05, it is experimentally determined that the highest CCA of 89.4% is attained with the parameter configuration \((\alpha,\beta,\gamma,\varphi,\tau)=(0.55,0.25,0.2,0.7,0.7)\). As discussed above, the basic mutually exclusive behavior set \(D_{b}\) comprises four behaviors: sitting, standing, walking, and lying. Given the mutual exclusivity of these behaviors, which dictates that only one behavior can occur at any given time, the final classification of an observed behavior is determined by identifying the behavior associated with the highest confidence score \(C_{b}\).