# Lightweight RT-DETR with Attentional Up-Downsampling Pyramid Network
## 2 Background and Literature Review
### Gesture-Recognition and Fall-Detection Methods Based on Deep Learning  
In deep learning, the YOLO algorithm has emerged as the most commonly used method for gesture recognition based on object detection, thanks to its high detection speed, accuracy, user-friendliness, and potential for further enhancements [48; 49]. As a CNN-based algorithm, YOLO has been widely adopted in real-world applications and has contributed to the evolution of other CNN-based algorithms. Many researchers have refined YOLO to create more accurate algorithms tailored to specific environments and use cases. For instance, [PERSON] et al. introduced an improved YOLOv8s algorithm for recognizing football referee gestures [50]; [PERSON] et al. implemented dynamic gesture recognition in medical scenarios using an enhanced YOLOv5 for aviation medical rescue flight simulators [51]; and [PERSON] et al. utilized YOLOv8 for gesture-controlled medical devices [52]. [PERSON] et al. proposed a fall-detection algorithm based on an improved YOLOv8 model for indoor environments [53], while [PERSON] et al. presented a home fall-detection algorithm based on an enhanced YOLOv5 [54].  
In industrial work environments, where relatively large spaces minimize background interference, these YOLO-based gesture recognition algorithms typically operate in relevant industrial contexts rather than in healthcare settings. The optimizations in these algorithms typically focus on accurately detecting hand targets rather than addressing background interference. While they perform well in industrial settings, the background interference in healthcare environments is more complex and variable, which can greatly impact their recognition accuracy. Although the YOLO-based fall-detection algorithms are somewhat similar to the healthcare environments discussed in this paper, the presence of medical devices and other items complicates their general applicability to gesture recognition tasks.  
However, alongside the widespread application and diversification of CNN-based algorithms, we should explore effective yet underdeveloped algorithms to ensure diversity and innovation in relevant academic fields, such as the Transformer-based RT-DETR, which is the focus of this paper. Moreover, despite the extensive application of the YOLO series in industrial production experiments, they still exhibit notable shortcomings. YOLO tends to produce numerous redundant bounding boxes during operation, which need to be eliminated in post-processing through non-maximum suppression (NMS). The choice of NMS hyperparameters can have a significant effect on YOLO's accuracy and speed [55]. [PERSON] et al. incorporated a dynamic encoder into the DETR model to mimic the Transformer encoder's attention mechanism, improving the model's ability to detect small object features [56]. They also introduced an unsupervised pre-training (UP)-DETR model, which accelerates convergence in object detection and helps alleviate the issue of long training times associated with DETR [57].  
Unlike the typical industrial work environments encountered in laboratories, healthcare environments are closer to home living situations; however, irregular obstructions (e.g., furniture, pets) scattered around the person and gesture present a significant challenge for YOLO models, which struggle to recognize holistic information. To achieve high precision and recall in healthcare settings while considering factors like cost and training time, it is advisable to shift from YOLO and opt for a lightweight model from the DETR series, such as the real-time end-to-end object-detection model RT-DETR, as the enhanced base model [55]. Unlike YOLO, RT-DETR utilizes a mixed encoder that efficiently processes multi-scale features and performs Intersection over Union (IoU) aware query selection, allowing it to achieve faster speeds with comparable accuracy and outperform YOLO in object detection under complex backgrounds.