Additionally, previous research has lacked a generalizable framework capable of adapting to varied tracking scenarios while maintaining efficiency and scalability in edge AI environments. These limitations highlight the need for a computationally efficient, scalable, and real-time framework for multi-camera tracking in distributed edge networks, which our work aims to address. To address this gap, we propose the TEDDY framework, which stands for \"An Efficient Framework for Target Tracking Using Edge-Based Distributed Smart Cameras with Dynamic Camera Selection\", an innovative and efficient solution for target tracking that leverages edge-based distributed smart cameras with context-aware dynamic camera selection. The key contributions of the TEDDY framework can be summarized as follows:  
* **Energy-Efficient Multi-View Target Tracking for Resource-Constrained Environments:** The TEDDY framework is designed to optimize power consumption in edge-based multi-camera tracking systems while ensuring accurate target tracking. By employing context-aware dynamic camera selection, it minimizes unnecessary computations and significantly reduces redundant processing, making it highly suitable for resource-limited settings. * **Scalability and Flexibility for Large-Scale Deployments:** The extensibility of the framework was demonstrated in the second experiment, where its application was expanded to a smart city scenario. The results highlight TEDDY's ability to efficiently manage computational resources in large-scale multi-camera networks, ensuring energy savings while maintaining accurate tracking in real-world applications. Figure 1 summarizes the proposed framework. The TEDDY framework operates through a network of distributed edge devices, each equipped with cameras or other sensors, strategically placed in the area where the target is expected to be. The central component of the system is the base station, which manages and coordinates the overall schedule of these edge devices. In Figure 1, the target is depicted as a person, and the system is shown with three edges. However, it is important to note that both the target and the number of edges are flexible and can vary, depending on the scenario. To demonstrate the effectiveness of the TEDDY framework, we present two practical applications that validate its utility. First, we conducted an experiment with a single-person tracking system. For our first experiment, we designed a dataset simulating a scenario in which the system tracks a specific target--a person following various routes at different speeds. We compared our system, which employs dynamic camera selection, to a system without this feature. Our results demonstrate that our system achieves over 20% better performance, in terms of computational complexity and energy efficiency, when applied to embedded boards. In the second experiment, we simulated a smart city scenario, to test our method with a dynamic traffic light adjustment system as an extension of the scenario in the first experiment. Specifically, we modeled a city where traffic lights can adjust in real time when an emergency vehicle is detected, allowing it to reach its destination as quickly as possible. In this experiment, our framework reduced computations by around 90% compared to the always-on camera system, while achieving the same accuracy. As evident from the experiments, the novelty of our approach lies in its ability to dynamically optimize resource allocation in multi-camera tracking while maintaining high accuracy, demonstrating a scalable and energy-efficient solution for real-world applications. The remainder of this paper is structured as follows: Section 2 summarizes related work in the field of general camera tracking, camera selection, and on-device tracking. Section 3 details the proposed TEDDY framework. Section 4 presents our experimental setup and two distinct test scenarios, and Section 5 shows the corresponding results. Section 6 provides an in-depth discussion of the findings and addresses potential limitations of the framework.