## 5 The Potential of Computer Vision Research in UAV Datasets
### Leveraging UAV Datasets for Computer Vision Applications
#### 5.1.1 Human Behavior Understanding and Gesture Recognition  
The UAV-Human platform is essential for utilizing UAVs to study human behavior, including a range of conditions and perspectives for pose estimation and action recognition. This dataset contains multi-modal information, including skeleton, RGB, infrared, and night vision modalities. Essential for UAV control and gesture identification, UAV-Gesture contains 119 high-definition video clips with 13 gestures for command and navigation that are marked with body joints and gesture classes. Because this dataset was captured outside, it has more practical UAV control applications because of the variations in phase, orientation, and body shape.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Employed Method** & **Name of the Dataset** & **Benefit from the Use of Method** \\ \hline Attention UNet28, ViT-Tiny, ViT-Small & RescueNet [6] & Improved disaster response strategies and enhanced model performance in segmentation tasks through transfer learning \\ \hline Fisheye-based action recognition approach, HigherHRNet, AlphaPose & UAV-Human [7] & Robust models for human behavior understanding \\ \hline ERNet & AIDER [8] & High performance with minimal memory requirements, suitable for real-time aerial image classification \\ \hline YOLOv3-Tiny, MobileNetv2-SSDLite & AU-AIR [9] & Real-time object detection on UAVs, bridging the gap between computer vision and robotics \\ \hline DenseNet-201, I3D-Inception-v1, TRN-Inception-v3 & ERA [10] & High performance in single-frame and video classification tasks \\ \hline Multi-Scale-Dilation net, FSO, 3D CRF & UAVid [11] & Enhanced semantic segmentation performance in urban scenes, addressing large-scale variation and moving object recognition \\ \hline Convolutional and connection layers, weight matrices, weighted pooling & VRAI [12] & Superior vehicle re-identification performance \\ \hline Aggregating tree-wise F1 scores, weighting coefficients for averaging F1 scores & FOR-instance [13] & Improved methods for individual tree segmentation, crucial for understanding forest ecosystems \\ \hline FDA-Net (Feature Distance Adversary Network) & VERI-Wild [14] & Enhanced discriminative capability in vehicle re-identification tasks \\ \hline Smooth silhouette loss & UAV-Assistant (UAVA) [15] & Improved performance in 3D pose estimation tasks \\ \hline Time-delay neural network, domain adaptation techniques & KITE [16] & Enhanced UAV command recognition systems through visual context and domain adaptation \\ \hline Pose-based Convolutional Neural Network (P-CNN) & UAV-Gesture [17] & High accuracy in gesture recognition for UAV control \\ \hline Spatial-Channel Transformer, curve projection model & DarkTrack2021 [19] & Improved nighttime UAV tracking accuracy by enhancing low-light images \\ \hline Illumination adaptive, anti-dark capabilities, efficient image enhancer & UAVDark135 [18] & Superior performance in all-day aerial object tracking, adaptability to different light conditions \\ \hline KeepTrack-optimized UAV-KT & BioDrone [20] & Addresses challenges in tracking tiny targets with drastic appearance changes, providing a robust benchmark for vision research \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of methods employed on UAV datasets and their benefits.