Video human trajectory prediction is a fundamental research task for many civil and defense applications. Human trajectory prediction in videos, especially in the context of unmanned aerial vehicles (UAVs) platforms, presents unique challenges due to the temporal dynamics and motion patterns inherent in non-stationary video data. As frames in a video streaming are highly correlated, trajectory detection in UAV images is affected by particular factors such as oblique camera views and the platform motion. This study aims to evaluate the performance of deep learning model’s predictions in the context of UAVs videos by comparing three distinct categories: classical machine learning, established deep learning architectures, and computationally efficient models based on Multi-layer Perceptrons (MLPs). We propose an analysis based on only bounding box center coordinates instead of image scenes. The results show that a simple linear architecture provided the best performance, highlighting the importance of these mechanisms in predicting human motion from trajectory data alone.