## 3 Results
The elevated false positive rate observed during evaluation remains a primary concern and a focus of ongoing investigation. Preliminary analysis indicates that the majority of false positives stem from objects exhibiting high visual similarity to the target class--particularly in shape, scale, or edge contours. These include environmental features such as tree trunks, branches, rocks, or man-made debris, which, when viewed from aerial perspectives and under specific lighting conditions, resemble the silhouette or structure of a human figure. To illustrate this phenomenon, qualitative examples of common false positives are provided in Figure 11 in RGB format (left) and in grayscale with Canny edge detection applied (right). These visualizations highlight the misclassified regions and their similarity to actual positive samples. The ongoing work includes refining the negative dataset, enhancing spatial context awareness, and improving the second-stage classifier to reduce false detection rates in future iterations of the framework. Beyond detection accuracy, another key consideration for field deployment is computational efficiency, particularly for real-time search and rescue applications. To further evaluate the computational performance of SARGAS, we conducted inference time tests \begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline SARGAS & 85.28\% & 95.58\% & 88.79\% & 92.06\% \\ YOLOv5 & 26.1\% & 72.02\% & 29.04\% & 41.39\% \\ Faster-RCNN & 99.6\% & 99.7\% & 99.9\% & 99.8\% \\ cutLER & 0\% & 0\% & 0\% & 0\% \\ \hline \end{tabular}
\end{table} 
Table 5: Algorithm performance check on unrelated to training data video. across four algorithms--SARGAS, YOLOv5, Faster R-CNN, and CutLER--on identical hardware and a 1438-frame video. For YOLOv5 and Faster R-CNN, input frames were resized to 640 \(\times\) 640 pixels during inference to match their respective training resolutions, as recommended by their official implementations. This likely contributed to their faster processing times. In contrast, SARGAS operated directly on full resolution 1080p frames. All tests were executed on a NVIDIA T4 GPU, 4 vCPUs, 16 GB RAM. While GPU was available, we chose to conduct the experiment using CPUs. In addition to detection performance, computational efficiency is a critical factor for real-world deployment. As shown in Table 6, YOLOv5 achieves the fastest runtime at approximately 3.145 FPS. While SARGAS is slower at 0.371 FPS, its runtime is comparable to that of Faster R-CNN (0.306  FPS), despite processing larger input frames and requiring no bounding box annotations or complex label structures. This highlights SARGAS's practical balance between speed and minimal supervision--making it well-suited for low-resource and time-sensitive scenarios. CutLER demonstrates a moderate runtime performance of 0.498 FPS, but it fails to generalize effectively in our use case, consistently missing small objects of interest such as distant or partially occluded humans in aerial imagery. This further emphasizes the challenge of applying generic unsupervised detectors to domain-specific tasks without targeted adaptation. Surprisingly, Faster R-CNN showed consistently high performance across both evaluation scenarios, while YOLOv5 experienced a notable drop in accuracy when tested on previously unseen footage. In contrast, SARGAS maintained stable performance, with