## 3 The Proposed Approach
### Multi-Source Feature Fusion Based on RS  
Following the localization and recognition of individuals and characteristic objects, it is imperative to establish their associations across both temporal and spatial dimensions. From a temporal perspective, consideration must extend beyond the current frame of the drivers being tracked. It is necessary to comprehensively analyze all the feature objects within the temporal span covered by the tracking drivers. This approach ensures a holistic understanding of the drivers' dynamics over time. Spatially, assessing the intersection between the bounding boxes of the tracking drivers and those of the feature objects is crucial for determining their spatial relationships accurately. Specifically, for a tracking target \(P_{i}\) in the frame \(t\), its associated feature object set \(O_{i}^{t}\) is  
\[O_{i}^{t}=\left\{o_{j}\middle|IoU\left(B_{p}^{t},B_{o}^{t}\right)>\delta\right\} \tag{1}\]  
where \(B_{p}^{t}\) denotes the bounding box of the tracked driver in the frame \(t\), while \(B_{o}^{t}\) represents the bounding box of a feature object appearing in the frame \(t\). \(IoU\left(B_{p}^{t},B_{o}^{t}\right)\) indicates the IoU between the driver and the abnormal object. The parameter \(\delta\) denotes the threshold, which is set to 0.3. Within a behavior sampling period, the associated characteristic object set \(O_{i}\) for \(P_{i}\) is defined as the union of all the frames' associated characteristic object sets in the sampled frame sequence \(\{F_{t-n},F_{t-n+1},\ldots,F_{t}\}\). \[O_{i}=\bigcup_{k=t-n}^{t}O_{i}^{k} \tag{2}\]  
where \(O_{i}^{k}\) represents the set of associated characteristic objects for \(P_{i}\) in the frame \(k\), and \(n\) denotes the sampling length for behavior recognition. By employing the spatiotemporal alignment module to detect and associate abnormal characteristic objects in each frame, the model can effectively mitigate the issue of insufficient sensitivity to instantaneous behaviors. Table 2 is a decision table formulated based on feature data extracted through the use of spatiotemporal association rules and behavior classification outcomes derived from the improved TSM. The condition attributes ensemble comprises:  
(1) The Confidence of Associated Objects. Denoted as \(C_{o}\), this metric represents the average confidence level of associated features of the same category within the sampling time frame, pertinent to the driver under observation. (2) The Coverage Rate of Anomalous Features. Specifically defined for objects such as cigarettes, \(S_{o}\) denotes the coverage ratio of associated anomalous objects during the behavioral sampling period of the driver under scrutiny. Mathematically, it is expressed as:  
\[S_{o}(cigarette)=\frac{n_{1}}{n} \tag{3}\]  
where \(n_{1}\) denotes the number of frames in which the associated feature object (e.g., cigarette) appears, and \(n\) represents the total number of sampled frames. An elevated feature coverage rate signifies a higher frequency of interactions between the observed drivers and the feature objects, which is positively correlated with the likelihood of the corresponding behavior's occurrence. (3) The Behavior Confidence. \(C_{b}\) refers to the output generated by the improved TSM model concerning the confidence level of the detected behaviors. The Anomalous Behavior Set \(D_{a}\) includes non-driving behaviors (smoking), non-driving behaviors (playing on a phone), distracted driving (smoking), and distracted driving (playing on a phone). The basic mutually exclusive behavior set \(D_{b}\) encompasses four fundamental states: sitting, standing, walking, and lying. These mutually exclusive behaviors are foundational for classifying various activities, providing a clear distinction between different types of behaviors observed within the study context.