# Lightweight RT-DETR with Attentional Up-Downsampling Pyramid Network
## 2 Background and Literature Review
### Gesture-Recognition and Fall-Detection Methods Based on Classical Machine Learning  
In the field of machine learning, classic machine learning methods have been widely applied in gesture recognition and fall detection. For example, [PERSON] et al. [41] extracted HOG and SIFT features from images, concatenated them, computed the correlation between test and training image matrices, and finally classified the test images using a KNN classifier. [PERSON] et al. [42] proposed using a Gaussian Mixture Model (GMM) for hand segmentation in color images. Fourier descriptors, Hu moments, and Scale-Invariant Feature Transform (SIFT) were used to process the segmented images to extract distinct features. PCA and Linear Discriminant Analysis (LDA) were utilized for dimensionality reduction before classification by a kernel support vector machine (SVM). [PERSON] et al. [43] developed a gesture recognition system that first employed Gaussian filtering to remove noise from images, then utilized the YCbCr color space for skin color detection, ultimately extracting features based on the hand's key points. [PERSON] et al. [44] developed a gesture recognition system that independently recognizes sign language, enabling the identification of single-handed static and dynamic gestures, two-handed static gestures, and finger-spelled words in Indian Sign Language (ISL) from real-time video. The system's gesture recognition module consists of three main stages: preprocessing, feature extraction, and classification. In the preprocessing stage, skin color segmentation is used to extract signs from real-time video; during feature extraction, Zernike moments are employed for keyframe extraction; and the resulting features are classified using support vector machines. [PERSON] et al. [45] utilized Gaussian Mixture Models (GMM) for foreground-background separation, employing changes in acceleration to determine falls. [PERSON] et al. [46] compared the impacts of five traditional machine learning algorithms--support vector machine, K-nearest neighbors, stochastic gradient descent, decision trees, and gradient boosting--using F-score as a metric for experimental analysis. Experiments show that decision trees perform well in classification tasks, but gradient boosting algorithms perform better in certain complex scenarios. [PERSON] et al. [47] employed an improved Vibe algorithm to extract human targets, isolating moving individuals and using support vector machines (SVM) to distinguish between falling behaviors and other low-height actions like sitting and lying down.  
It is evident that early machine learning-based methods for gesture recognition and fall detection often required developing algorithms tailored to specific environments, resulting in limited applicability and robustness. The design of the algorithm is intricate, and these methods usually recognize only specific gestures or human features in controlled environments, lacking flexibility for deployment and resilience against strong interference. Additionally, their accuracy tends to be lower, which makes it difficult to meet practical production demands. To address these shortcomings, convolutional neural networks (CNNs) have been increasingly used in gesture recognition. CNNs are capable of extracting target features through multiple layers of convolutional and pooling operations, providing high robustness and real-time performance in deep learning models, such as DETRs, which outperform YOLOs in real-time object detection (RT-DETR) and You Only Look Once (YOLO).