## 4 Experimental Settings
### Algorithms
#### 4.1.3 Camera Selection
This process dynamically tracked the target and ensured that the most appropriate camera was activated at each step. Through this methodology, camera selection was effectively implemented within the camera network. The experiments were conducted using a self-collected dataset comprising footage from five strategically positioned cameras, as outlined in Table 1a,b. These cameras were installed at key locations within the experimental environment, to ensure optimal coverage of the target object as it navigated through multiple predefined routes. Figure 6 illustrates the layout of these routes and the placement of the cameras, which were designed to simulate realistic scenarios involving various behavioral patterns. Each route in the dataset was specifically designed to encompass three types of target behaviors: walking, running, and staggering. The walking behavior represented a consistent and steady movement, reflecting the most common and predictable gait pattern observed in pedestrian monitoring. The running behavior involved rapid movement, which presented a greater challenge for accurate tracking, due to increased speed and shorter intervals between camera transitions. The staggering behavior introduced irregular  
Figure 5: (**a**) The direction \(a_{t}\) of the target tracked in the edge device. (**b**) The representation of \(a_{t}^{\prime}\), which was a rotated version of \(a_{t}\), along with \(a_{1},a_{2},a_{3}\). and unpredictable movement patterns, characterized by abrupt changes in direction and unstable trajectories, further testing the system's robustness. These three behaviors were selected based on their relevance to real-world surveillance applications. Walking is frequently analyzed in pedestrian tracking, while running is often associated with suspicious activities, emergency responses, or law enforcement scenarios, making it a crucial behavior for monitoring systems. Staggering is particularly significant in health monitoring, anomaly detection, and security surveillance, where identifying erratic behavior can be essential for intervention. By incorporating these distinct movement patterns, the TEDDY framework was evaluated across diverse tracking conditions, demonstrating its adaptability to various real-world scenarios. The dataset included a comprehensive collection of frames for each route and behavior type, providing sufficient diversity for experimental analysis. The detailed frame counts for each route and behavior type are presented in Table 1b. Additionally, Figure 7 showcases illustrative examples of each behavior type along the various routes, offering visual insight into the complexity and variety of the scenarios captured in the dataset. This well-structured and diverse dataset forms the basis for evaluating the performance of the TEDDY framework in dynamic and challenging environments. \begin{table}
\begin{tabular}{l c} \hline \multicolumn{3}{c}{**(a) Dataset Summary**} \\ \hline
**Item** & **Value** \\ \hline Number of cameras & 5 \\ \hline Video resolution & 640 \(\times\) 480 \\ \hline Frame rate (FPS) & 30 \\ \hline Total frames & 106,677 \\ \hline Number of routes & 4 \\ \hline Number of behaviors & 3 \\ \hline Total tracks & 12 \\ \hline Environment details & Kwangwoon University \\ \multicolumn{3}{c}{Sae-bit building} \\ \hline \multicolumn{3}{c}{**(b) Frame Counts by Route/Behavior**} \\ \hline
**Route** & **Behavior** & **Frame Count** \\ \hline  & Walking & 451 \\ Route 1 & Running & 271 \\  & Staggering & 511 \\ \hline  & Walking & 781 \\ Route 2 & Running & 301 \\  & Staggering & 721 \\ \hline  & Walking & 991 \\ Route 3 & Running & 661 \\  & Staggering & 1321 \\ \hline  & Walking & 751 \\ Route 4 & Running & 541 \\  & Staggering & 991 \\ \hline  & Walking & 751 \\ Route 4 & Running