## 2 Materials and Methods
### Camera Motion Suppression  
While the use of background subtraction represents an innovative approach to mitigating the misclassification of moving objects, it is not without limitations. One of the primary challenges lies in its sensitivity to background variability, which becomes particularly pronounced when dealing with video footage captured from a moving platform. This issue is highly relevant in our context, as the visual material used in this study is recorded from a drone in motion. Prior research highlights this limitation, noting that video captured from stationary sources--such as CCTV cameras mounted on buildings, poles, or other fixed structures--tends to yield more reliable results than footage from dynamic platforms such as drones or helicopters [44].  
To address this challenge, [PERSON] proposed a solution known as Camera Motion Cancelation (CMC). According to the author, effective object tracking--particularly human tracking--requires suppression of camera-induced motion. This can be achieved through frame (or image) registration, a process in which multiple frames are geometrically aligned by designating one frame as the reference (fixed) image and transforming subsequent frames to match it. Affine transformation, a widely used registration technique, is capable of compensating for translation, rotation, scaling, and shear, and is therefore well-suited to this task [45; 46].  
In our implementation, we adopt a partial affine transformation approach, which offers a practical trade-off between computational efficiency and alignment precision. This method reduces variability in the background across frames, significantly improving the effectiveness of the MOG2 background subtraction and enhancing the reliability of the detection process. Moreover, the partial affine approach contributes to a more lightweight and faster-performing model when compared to full affine transformation methods.  
In the context of this work, the partial affine transformation is defined as comprising translation, rotation, and scaling, while explicitly excluding shear and reflection components. This decision was informed by a series of inference tests in which each affine transformation component was individually evaluated. The results indicated that shear and reflection introduced undesirable geometric distortions, which degraded the accuracy of background subtraction and reduced the stability of object detection. These distortions resulted in inconsistencies in bounding box localization and a decline in overall classification accuracy.  
While affine transformation is an effective method for image stabilization, it, like the motion detection method, presents certain limitations. In particular, affine transformation requires a set of consistent image points between sequential frames to achieve reliable alignment. Although this can be accomplished manually by selecting key points at the start of video processing, such an approach proves ineffective in scenes with dynamic backgrounds. It contradicts the objective of creating an automated detection pipeline. A more viable solution involves the use of automated feature-matching algorithms--such as SIFT, SURF, or ORB--to extract and track key points across frames, enabling dynamic and adaptive transformation without manual intervention.  
To the best of our knowledge, the integration of a selectively constrained partial affine transformation--empirically validated and applied within a multi-stage detection pipeline optimized for aerial footage--represents a novel contribution. This selective approach enables effective motion compensation while maintaining high computational efficiency, making it particularly well-suited for real-time aerial surveillance and object detection applications.