## 5 Results  
### Experiment 1: Single-Person Tracking  
In the first experiment, we evaluated the TEDDY framework by comparing its performance to a multi-camera target tracking system, where all cameras remain continuously active. In the TEDDY framework, cameras located at entry points operate continuously, to ensure that no individual entering or exiting the building is missed. Once a target is detected, the framework dynamically activates only the cameras expected to capture the target, based on its movement and predicted trajectory. The remaining cameras are deactivated, significantly reducing unnecessary operations and improving resource efficiency. In contrast, the always-active camera method processes data from all cameras continuously, regardless of the target's position. While this ensures comprehensive coverage, it results in substantially higher resource consumption and computational load, due to the constant activity of all the cameras. By dynamically managing camera activation, the TEDDY framework demonstrates a more efficient approach to resource allocation while maintaining accurate tracking capabilities. Both methods were tested using the same dataset and under identical conditions, to compare their outcomes. In each experiment, power consumption and tracking performance were measured, to evaluate the efficiency of the TEDDY framework. The experimental results shown in Figures 10-13 provide a detailed analysis of power consumption and efficiency across various routes and behavior types. The results clearly demonstrate that the TEDDY framework significantly reduces energy usage compared to the traditional method, where all cameras remain continuously active. Specifically, the TEDDY framework achieved a 20.96% reduction in power consumption, highlighting the effectiveness of its dynamic camera selection mechanism. This reduction was a direct result of the framework's ability to deactivate unnecessary cameras and ensure that only the cameras relevant to the target's trajectory remained active, thus enhancing overall resource efficiency. To further evaluate consistency, we performed ten repeated trials for each route and behavior type, measuring the power consumption (in mAh) of each edge device. Table 2 presents the means and standard deviations of these trials for Routes 1-4 with the TEDDY framework activated. Each group corresponded to a set of scenarios covering different movement behaviors (walking, running, and staggering). The average indicates the typical power draw under each condition, whereas the standard deviation quantifies how consistently the system performed across repeated runs. Lower standard deviations suggest more stable energy usage, whereas higher values indicate greater variability. In addition to power savings, the TEDDY framework was evaluated for its tracking accuracy in comparison to the always-active camera approach. The findings indicate that both methods achieved identical performance, in terms of maintaining consistent and reliable tracking until the completion of the target's trajectory. This demonstrates that the TEDDY framework achieves significant energy efficiency without compromising the tracking performance. The ability of the TEDDY framework to effectively balance energy optimization and operational accuracy validates its potential as a robust and practical solution for multi-camera tracking systems in resource-constrained environments. The results reaffirm that the framework successfully optimizes energy usage while preserving high tracking effectiveness across diverse scenarios.