## 4 Experimental Settings
### Algorithms
#### 4.1.2 Re-ID and Target Tracking  
As illustrated in Figure 3a, the process began with the calculation of the intersection over union (IoU) between the detection result from the current iteration and that from the previous iteration. The IoU value served as a measure of overlap between the two detections. If the IoU exceeded a predefined threshold, which was a hyperparameter tuned during the experiment, the framework assumed that the detected object was the same target from the previous iteration. We tuned this threshold by testing values from 0.3 to 0.9 in 0.1 increments on a small validation subset. Thresholds at or below 0.5 led to increased false associations, while thresholds at or above 0.8 resulted in more missed associations. An IoU threshold of 0.7 provided the best balance, ensuring stable object associations while minimizing unnecessary Re-ID computations. In this case, it directly proceeded to the direction calculation process, bypassing additional verification steps. However, if the IoU did not exceed the threshold, the system initiated the Re-ID process, to confirm whether the detected object matched the target, as detailed in Figure 3b. In the Re-ID process, the detected individual was cropped from the image, and the feature map of the detected area was extracted using the Omni-Scale Network (OSNet). Specifically, we utilized the OSNet x1.0 model from the TorchReID library [32; 33; 34], which offered superior performance relative to the other available models in the library. OSNet is particularly well suited for edge device implementations, due to its efficiency, as it has 2-to-10-times fewer parameters compared to models such as ResNet or MobileNet, making it significantly more lightweight. However, while OSNet is highly optimized for person re-identification, it may not generalize as well to more diverse image domains. In contrast, larger models, such as ResNet, often provide higher accuracy and robustness across varied tasks, though at the cost of increased computational and memory demands. This trade-off made OSNet an efficient choice for our resource-constrained edge devices, despite some limitations in absolute performance and adaptability. After feature extraction, the system computed the cosine similarity (defined as the normalized dot product of two vectors that measures the cosine of the angle between them) between the candidate feature map (obtained from the current detection) and the target feature map stored in the local database. This metric quantified the degree of directional alignment between the two feature vectors, meaning it assessed how similarly the image features were oriented. In practical terms, the directional alignment of the image feature vectors indicated how well the underlying visual patterns matched. If two images contained the same object, their feature vectors tended to be closely aligned, resulting in a higher cosine similarity value. If the computed cosine similarity exceeded a predefined threshold (a tunable hyperparameter), the framework determined that the detected object matched the target, and it proceeded to the direction calculation process. Conversely, if the similarity fell below the threshold, the process terminated without further actions. Through experimentation, we observed that setting the cosine similarity threshold below 0.5 often led to incorrect ID assignments, where different objects were mistakenly assigned the same identity. Conversely, setting the threshold above 0.8 caused inconsistency in re-identification, due to variations in lighting conditions, resulting in failure to assign a consistent ID even to the same object. To balance these trade-offs, we selected an optimal similarity threshold based on empirical results. Similarly, following the same approach as for the IoU and cosine similarity thresholds, we tuned the threshold by testing values from 0.3 to 0.9 in 0.1 increments on a small validation subset. In Experiment 1, we applied a Re-ID threshold of 0.6, while in Experiment 2 we used a Re-ID threshold of 0.7.