## 4 Experimental Settings
### Algorithms
#### 4.1.3 Camera Selection  
The camera selection process was deployed to activate edge devices based on the movement of a tracked target. To implement camera selection, the spatial relationships of the camera network were first represented using matrices. The network was visualized from the top view, and each camera location was marked as \"1\" on an \(n\times m\) matrix, to create the first matrix, \(A\). This matrix visually represented the camera layout within the network. The second matrix, \(B\), was an adjacency matrix that expressed how the cameras were interconnected (see Figure 4c). Camera numbers were assigned sequentially from the top-left to the bottom-right corner of matrix \(A\), and these numbers were used as indices in matrix \(B\) to organize the connectivity information. An example of the camera network and the method for assigning indices can be seen in Figure 4a,b. Camera selection was performed by utilizing directional vectors between nodes in matrix \(A\). For example, when a target moved from the area covered by Camera 2 to the area covered by Camera 1, the directional vector from Camera 2 to Camera 1 was computed based on matrix \(A\). Normalizing this vector to a magnitude of 1 yielded a directional vector, \(a_{1}\), of \((-\sqrt{2}/2,\sqrt{2}/2)\). Similarly, all directional vectors from Camera 2 to its connected nodes (e.g., Cameras 1, 4, and 3) were calculated. For example, the directional vector from Camera 2 to Camera 4, \(a_{2}\), was \((-\sqrt{2}/2,-\sqrt{2}/2)\), and the vector from Camera 2 to Camera 3, \(a_{3}\), was \((1,0)\). These vectors represented the spatial relationships between the nodes. Next, the actual movement direction of the target was compared with the directional vectors between the camera nodes, to determine the next camera. When a target moved within the view of Camera 2, the average movement direction over the last five frames was calculated, to obtain \(a_{l}\). This process is visually confirmed in Figure 5a. Since the camera views differed in orientation, \(a_{l}\) was rotated to align with the camera's perspective and normalized to produce \(a_{l}^{\prime}\). This normalized vector, \(a_{l}^{\prime}\), represented the target's movement direction in the camera network, considering the camera's orientation. Subsequently, the cosine similarity (defined as the normalized dot product of two vectors that measures the cosine of the angle between them) was calculated between \(a_{l}^{\prime}\) and the directional vectors \(a_{1}\), \(a_{2}\), and \(a_{3}\) derived for Camera 2. In physical terms, this metric quantified how well the target's movement direction (represented by \(a_{l}^{\prime}\) aligned with the candidate camera directions. In other words, it evaluated which camera's orien  
Figure 4: (**a**) The top view of a camera network. The positions of the cameras in (**a**) were modeled on an \(n\times m\) matrix (\(n=3,m=4\)), as shown in (**b**). The locations marked with “1” in (**b**) were treated as nodes, and the adjacency matrix of matrix \(A\) was then represented in (**c**) as matrix \(B\). tation best matched the predicted trajectory of the target. For instance, given an initial movement vector  
\[a_{t}=(1,0)\xrightarrow{\text{rotate}}a_{t}^{\prime}=(0,1)\]  
the directional vectors of the connected cameras were defined as follows:  
\[a_{1}=\left(\frac{\sqrt{2}}{2},\frac{\sqrt{2}}{2}\right),\quad a_{2}=\left(- \frac{\sqrt{2}}{2},\frac{\sqrt{2}}{2}\right),\quad a_{3}=(1,0).\]  
The cosine similarity between \(a_{t}^{\prime}\) and each directional vector was then computed:  
\[\text{Cosine similarity with }a_{1}=0.866,\] \[\text{Cosine similarity with }a_{2}=0,\] \[\text{Cosine similarity with }a_{3}=-0.866.\]  
As depicted in Figure 5b, \(a_{t}^{\prime}\) and the directional vectors \(a_{1}\), \(a_{2}\), and \(a_{3}\) represented in matrix \(A\) could be intuitively visualized. The edge corresponding to the vector with the highest cosine similarity was activated to select the next camera, which in this case was Edge 1 corresponding to \(a_{1}\).