Skeleton detection deep learning. Usually, human pose estimation .

Skeleton detection deep learning This paper presents a comprehensive survey of pose-based applications utilizing deep learning, encompassing pose esti-mation, pose tracking, and action recognition. 4, also offer valuable insights into the model's interpretability, which is valuable to ensure transparent and trustworthy decision-making in deep-learning based fall detection solutions [56]. DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images, IEEE Trans on Image Processing Hi-Fi: Hierarchical Feature Integration for Skeleton Detection, IJCAI2018 Oct 18, 2023 · Regarding the manuscript entitled " Skeletal Fracture Detection with Deep Learning: A Comprehensive Review in which The authors present a comprehensive review of 40 recent papers from prestigious databases, including WOS, Scopus, and EI, to analyze and evaluate the use of deep learning models in diagnosing skeletal fractures from X-ray images. Oct 20, 2024 · The remainder of this paper is organized as follows. Appearance-based Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions Pratik K. Mar 1, 2025 · In this study, we use YOLOv11, a state-of-the-art real-time object detection model, as the foundation for a multi-task learning framework for fracture detection and classification. Khan video anomaly detec-tion mostly utilize videos containing identifiable facial and appearance-based features. g. Many recent methods frame object skeleton detection as a binary pixel classification problem, which is similar in spirit to learning-based edge detection, as well as to se-mantic segmentation methods. Perales 2,* , José Maria Buades 2 , Noureddine Boujnah 3 and Sep 18, 2024 · We proposed a skeleton-based fall detection approach using angle features and a machine learning approach to overcome the problem. DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images Abstract Object skeletons are useful for object representation and object detection. py using Keras and Tensorflow. They are complementary to the object contour, and provide extra information, such as how object scale (thickness) varies among object parts. In the procedure, we first extracted the whole-body landmark key points using a media pipe and then we calculated the 70-angle features and employed the Boruta feature selection approach to select the potential INDEX TERMS 3D skeleton, action recognition, deep learning, fall detection, embedded system, image processing. This work introduces a low-cost human skeleton detection network for detecting human skeleton shapes in real time. To address this challenge, we propose a novel deep learning framework integrating convolutional neural networks (CNNs), multi In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. However, the existing methods struggle with sparse radar point cloud data, leading to inaccuracies in joint localization. Firstly, a lack of clear definitions for recognition, classification, detection, and localization tasks hampers the consistent development and comparison of methodologies. The skeletons are basically a set of With the use of an intelligent video system, this research provides a method for detecting abnormal behavior based on the human skeleton and deep learning. The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. In recent years, CNN algorithms have been used for skeleton extraction. May 27, 2023 · Therefore, determining the current results of these studies is very important in selecting solutions and developing commercial products. Among all these, Human skeletons carry the most significant amount of information with minimum noise. Try our advanced platform now! Sep 1, 2024 · Highlights • We present a deep learning framework to identify and quantify skeletal grains in carbonate cores. Vision-based method is an approach that can be used for detecting human falls from video surveillance system. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in Code for our CVPR2016 paper " Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs " and TIP paper " DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images ". Apr 28, 2019 · An introduction to the techniques used in Human Pose Estimation based on Deep Learning. In this survey, we first introduce the fall detection techniques categories, then we introduce the process of fall detection machine learning model and the commonly used 3d skeleton data fall detection. Dec 15, 2024 · Top-down methods are characterized by higher accuracy but suffer from a restricted capability for single-person skeleton detection at a time. Because pose estimation is an easily applicable computer vision technique, we can implement a custom pose estimator using existing architectures. We present a novel taxonomy of algorithms based on the Dec 30, 2022 · In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. In the present As deep learning technology advances, human fall detection (HFD) leveraging convolutional neural networks (CNNs) has recently garnered significant interest within the research community. Nov 24, 2020 · However, applying deep learning frameworks to 3D human skeleton extraction from point clouds remains challenging because of the sparsity of point clouds and the high nonlinearity of human skeleton regression. Still, numerous challenges need to be resolved. These approaches allow the detection of various types of activities such as walking, running, jumping, jogging, or falling, among others. Aug 13, 2025 · Due to their complexity, these networks often come with a large number of parameters and computational complexity [5]. Human action can be recognized from its appearance, geometrical shape, joint variations and body skeleton. For instance, recurrent neural networks (RNNs) with Long-Short Term Memory (LSTM) have been employed to model skeleton data for 3D action recognition [4 - 7]. It also enables transfer learning and joint training across different action tasks and datasets, which result in performance improvement. Feb 18, 2024 · For decades, people have been developing skeleton detection techniques. The data accepted into the backbone are diversified through CSPDarkNet-53. skeleton extraction. The existing reviews often lack technical depth or have limited With the population of CNNs, deep learning-based algo- rithms achieved great success in image classification, object detection, image segmentation and image transformation, etc. We removed the skull using a trained deep-learning bet model. Some have focused on improving Feature represen-tation of skeleton joints. • Despite the current popularity of one-stage detectors in the wider computer science community, the two-stage detector provides the most robust performance for geological applications. Abstract Computing object skeletons in natural images is chal-lenging, owing to large variations in object appearance and scale, and the complexity of handling background clut-ter. Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions Pratik K. In vision-based method, the use of skeleton features, which are the positions of the This study aims to leverage deep learning technology to utilize the vast amount of surveillance video data to explore how to detect human skeletal keypoints at electric power work sites under low-light conditions, involving technologies such as low-illumination image brightness enhancement and the design of human skeletal keypoint detection Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model Neziha Jaouedi 1 , Francisco J. The use of videos wi In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. Abstract Implementing skeleton-based action recognition in real-world applications is a difficult task, because it involves multiple modules such as person detection and pose esti-maton. In this paper, we propose an efficient approach for activity recognition in videos with key frame extraction and deep learning architectures, named KFSENet. In this study, we develop a deep learning-based approach for 3D human skeleton extraction from point clouds. This chapter entails all the fundamental aspects of skeleton-based action recognition, such as—skeleton tracking, representation, preprocessing techniques, feature extraction, and recognition methods. Oct 4, 2023 · Therefore, deep learning techniques brought significant advances and performance gains in pose estimation tasks. The advent of deep learning has significantly improved the accuracy of pose capture, making pose-based applications increasingly practical. In this paper, we conduct a comprehensive study of various data augmentation techniques specific to skeletal data, which aim to improve the accuracy of deep learning models. Oct 18, 2023 · In conclusion, this review fills the gap in precise task definitions within deep learning for bone fracture diagnosis and provides a comprehensive analysis of the recent research. In this study, we optimise a dual-stream architecture that combines image classification and skeleton recognition models to analyse video data for body motion analysis. Human body skeleton detection an tracking from video camera in real time. , image points in spatial context, and hence the implied object boundaries), resulting in precise skeleton detection. Despite the advancements, existing approaches struggle to address the issues of occlusions and limited annotated data. The use of videos wi Advanced Camera-Based Scoliosis Screening via Deep Learning Detection and Fusion of Trunk, Limb, and Skeleton Features In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. However, most existing shape and image Jan 2, 2025 · We propose a novel, deep-learning-based approach to automatic detection of 3D landmarks in CT images of the lower limb. Video of the subject’s whole body while walking along a straight path is recorded, then gait landmark sequences are detected and corrected. The generated model is saved in model folder. Jun 30, 2023 · An intelligent gait parameter analysis system is proposed based on deep learning and human skeleton detection in videos. • Abstract:Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. , 2021, Liu et al. Sep 1, 2022 · The skeleton keypoints detection network improves recognition accuracy by cascading, with each stage having a part in intermediate supervision, thus preventing gradient disappearance in the deep network [18], [19]. It was developed for an Orbbec Astra camera DRGB (Depth-RGB), uses the OpenNI2 driver, it also works with Asus Xtion and Prime sense. We present a novel taxonomy of algorithms based on the Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions Pratik K. Nov 1, 2024 · Violence detection tasks can be divided into two categories: recognition based on appearance features and recognition based on key points of the human skeleton. Our current method is essentially categorized as “top-down”. An overview of how human pose estimation problem is being developed can be summarized with Table 1 below [7]. Dec 1, 2020 · Multiple deep learning-based skeleton detection models have been proposed, while their robustness to adversarial attacks remains unclear. Aug 9, 2021 · The human skeleton or deep learning framework is useful for accurately recognizing human behavior and analyzing that behavior across different situations. Nov 26, 2022 · Among them, the detection and extraction of human skeleton in a dance video based on this technology has a huge market demand in education and training. We will briefly go over the architecture to get an idea of what is going on under the hood. Real-time object detection with YOLOv7 for people detection in smart city systems. Dec 20, 2023 · In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. INTRODUCTION Sep 5, 2021 · Posenet is a real-time pose detection technique with which you can detect human poses in Image or Video. , 2022, Tran et al. Mixed reality (MR) can be adopted to address this by involving inspectors in various stages of the assessment process. Oct 18, 2023 · Abstract Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. But in Human pose estimation technology, we can detect humans and analyze the posture of that particular human. With the remarkable success of deep learning in computer vision problems, such as segmentation, detection, classification, etc. Oct 1, 2021 · Du et al. It helps to analyze the activity of a human. However, challenges remain that hinder progress in this field. Utilize the appearance information in videos (Islam et al. The use of videos wi A. We will explain in detail how to use a pre-trained Caffe model that won the COCO keypoints challenge in 2016 in your own application. An overview of how human pose estimation problem is being developed can be summarized with Table1 below [7]. The human skeleton or deep learning framework is useful for accurately recognizing human behavior and analyzing that behavior across diferent situations. Despite the advancements in skeleton topology, accuracy in detailing skeletal parts remains challenging, with specific issues such as jagged edges in high-resolution images. Bottom-up methods have lower accuracy but are beneficial for multi-person skeleton detection and, thus, have a higher speed for the full frame. From these frames, we extract key points using Dec 31, 2022 · In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. To address this, we first introduce an image preprocessing (IPP) module, which Dec 31, 2022 · In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. This work intro-duces a low-cost human skeleton detection network for detecting human skeleton shapes in real time. Motivated by advancements in computer vision, deep learning, and camera technologies, people have gained huge progress in skeleton detection with one after another improved method. This paper proposes a novel framework In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. May 25, 2022 · Many works dedicated to human activity detection from video cameras can be found in the literature. The use of videos wi Sep 1, 2020 · This is based on two major stages: the first is the extraction of 2D features using skeleton detection and human pose estimation; the second is the pre-training of a new deep learning model based on activity classification. The proposed vision-based fall detection system is composed with skeleton information extraction by traditional algorithm, and action recognition made by neural network a Jun 27, 2022 · With the use of an intelligent video system, this research provides a method for detecting abnormal behavior based on the human skeleton and deep learning. Jun 23, 2025 · Human skeleton estimation using Frequency-Modulated Continuous Wave (FMCW) radar is a promising approach for privacy-preserving motion analysis. Abstract: Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. Dec 28, 2024 · The method trains an automatic encoder-decoder deep neural network model by means of a homemade synthetic dataset, which maps the 2D human skeletal key point sequence samples extracted from motion This is based on two major stages: the first is the extraction of 2D features using skeleton detection and human pose estimation; the second is the pre-training of a new deep learning model based on activity classification. Real-time online skeleton detection. Dec 28, 2024 · Specifically, the skeleton key point information extracted based on OpenPose is fused into the encoder-decoder network for rough detection of the human body target, and the residual refinement network is used to fine-adjust the human body matting, so as to achieve accurate human contour extraction. This paper list will be continuously updated at the end of each month. However, its performance is reduced in processing video stream input. Performance comparison of skeleton pixel detection and skeleton point datasets from leaderboard. In this paper, we investigated the improved detection method to estimate the position of the head and shoulder key points and the acceleration of position change In object detection technology, we can detect Humans, but we can’t say the activity of that human. However, challenges remain that hinder progress in this field. Jan 27, 2024 · With the remarkable development and outstanding performance of deep learning methods in various computer vision tasks, such as image classification [42, 22] and object detection [9, 152], the application of deep learning to skeleton data for action recognition has gained prominence. Dec 24, 2024 · The paper [14] presents an efficient deep learning model for recognizing actions from skeleton data, with significantly fewer trainable parameters compared to traditional CNN models. , many researchers are developing diagnostics systems based on deep neural networks. Skeleton-based action recognition models in PyTorch, including Two-Stream CNN, HCN, HCN-Baseline, Ta-CNN and Dynamic GCN. Usually, human pose estimation Dec 28, 2024 · Abstract Recent advancements in skeleton extraction have significantly improved the process by simplifying the skeleton regression task into graph component detection. This repository contains a comprehensive solution for human activity recognition in video data using skeletal keypoint extraction and deep learning. To begin with, the spatiotemporal features of human bones are extracted through iterative In this review, we only focus on the recent deep learning-based algorithms for skeletal video anomaly detection and did not include traditional machine learning based approaches. To begin with, the spatiotemporal features Nov 24, 2022 · A deep learning approach based on skeleton data for recognizing specific VAs that are highly relevant in surveillance scenarios is proposed. It works with normal webcam too, in the case of background is smooth and white DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Xiang Bai and Alan Yuille Abstract—Object skeletons are useful for object representation and object detection. Keywords: deep learning; depression recognition; human skeleton; kinect sensor; temporal convolution network. The use of videos wi May 16, 2024 · With the remarkable development and outstanding performance of deep learning methods in various computer vision tasks, such as image classification [47, 48] and object detection [49, 50], the application of deep learning to skeleton data for action recognition has gained prominence. Feb 1, 2025 · The learned representations of the three models, visualized using t-SNE in Fig. We present a novel taxonomy of algorithms based on the various learning approaches. Jul 2, 2024 · Among different data modalities, skeleton data offers compact representation and computational efficiency. Prisdl is ours. Section 2 reviews the relevant literature, including standard motion assessment and determination of motor delay, the application of artificial intelligence techniques in medical treatment and motion detection, machine learning and deep learning, and human skeletal detection. Firstly, a lack of clear definitions for recognition, classification, detection, and Jun 30, 2022 · For example, in the change from severe depression to moderate or mild depression multi classification dataset. May 1, 2022 · In recent years, with the development of deep learning technology, the detection effect of human skeleton keypoints has been constantly improved, and it has been widely used in related fields of computer vision. let's make a real-time project. Beyond Joints: Learning Representations from Primitive Geometries for Skeleton-based Action Recognition and Detection (TIP 2018) [paper] [Github] [DPRL] Deep progressive reinforcement learning for skeleton-based action recognition (CVPR 2018) [paper]. Oct 15, 2019 · on 3D Skeleton for Deep Learning Technique TSUNG-HAN TSAI , (Member, IEEE), AND CHIN-WEI HSU Department of Electrical Engineering, National Central University, T aoyuan City 32001, Taiwan Apr 1, 2019 · The aim of this study was to develop a deep learning-based method for segmentation of bones in CT scans and test its accuracy compared to manual delineation, as a first step in the creation of an automated PET/CT-based method for quantifying skeletal tumour burden. The framework consists of the following modules: data preprocessing, feature extraction, forecasting and classification. Furthermore, we see skeleton GTs used not only for training skeleton detectors with Convolutional Neural Networks (CNN), but also for evaluating skeleton-related pruning and matching algorithms. In recent years, much work has gone into developing a robust and accurate deep-learning framework for skeleton-based HAR. In their methodology, they have divided the human skeleton obtained from the Kinect sensor into five different parts. Table 5. Oct 26, 2023 · Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. Apr 28, 2023 · Thanks to the deep learning technologies in recent years, detection accuracy in key points of human skeleton has been continuously improved, especially in research and design of static images in the early stage. Pose estimation involves the determination Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. This paper integrates You Only Look Once (YOLO) v5n and YOLO v5m The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. Moti- vated by advancements in computer vision, deep learning, and camera tech- nologies, people have gained huge progress in skeleton detection with one after another improved method. Jan 18, 2024 · In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in This illustrates the robustness of the proposed method in accurately identifying change points and segmenting continuous skeleton-based activities as compared to two other state-of-the-art techniques: one based on deep learning and another using the classical time-series segmentation algorithm. Sep 13, 2016 · Object skeletons are useful for object representation and object detection. Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions: Paper and Code. However, these systems cannot work interactively with human inspectors. Mishra, Alex Mihailidis, Shehroz S. Oct 18, 2023 · Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. The existing reviews often lack technical depth or In this paper, we examine the most recent advances in skeleton-based fall detection in RGB videos, from handcrafted feature-based methods to advanced deep learning algorithms. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in Nov 5, 2024 · Additionally, we propose a Swin Transformer-enhanced CMU-Pose to extract human skeleton features (HSF), identifying skeletal asymmetries crucial for SS. But object skeleton extraction from natural images is very challenging, because it requires the extractor to be able to capture both local and non-local image context in order to With the rise of deep learning, researchers have recently formulated skeleton detection as image-to-mask classi・…ation problem by using learned weights to fuse the multi-layer convolutional features in an end-to-end manner. Meanwhile, a vision-based fall detection system has shown some significant results to detect falls. In this paper, we perform a full survey on using deep learning to recognize human activity based on three-dimensional (3D) human skeleton data as input. As a uni-fied solution, SkeleTR can be directly applied to multiple skeleton-based action tasks, including video-level action classification, instance-level action detection, and group-level activity recognition. Khan Abstract—The existing methods for video anomaly detec-tion mostly utilize videos containing identifiable facial and appearance-based features. In this paper, we examine the most recent advances in skeleton-based fall detection in RGB videos, from handcrafted feature-based methods to advanced deep learning algorithms. Feature representation of skeleton joints and the modeling of temporal dynamics to recognize human actions In computer vision, many studies have taken an interest in developing deep learning methods for the analysis of human activity. Deep learning algorithms are always applied in X-rays and CT image processing, such as assessing the mineral bone density (BMD), detecting bone fractures, and recommending treatment Abstract A recent study to determine the fall is focused on analyzing fall motions using a recurrent neural network (RNN), and uses a deep learning approach to get good results for detecting human poses in 2D from a mono color image. The use of videos wi Abstract Accurate fall detection for the assistance of older people is crucial to reduce incidents of deaths or injuries due to falls. An An integrated integrated deep deep learning learning and image and image processing-based processing-based crack detection crack detection and skeleton and extraction. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. The paper reviews state-of-the-art methods for skeleton-based HAR. The model consists of three hidden layers and a Softmax output layer to conduct a 5-class classification. May 16, 2024 · With the remarkable development and outstanding performance of deep learning methods in various computer vision tasks, such as image classification [47, 48] and object detection [49, 50], the application of deep learning to skeleton data for action recognition has gained prominence. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in Feb 13, 2024 · Data augmentation is a key technique to enhance model generalization and robustness in deep learning while suppressing overfitting to training data. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in May 30, 2025 · Various types of research have been conducted to detect ASD through deep learning, including facial feature analysis, eye gaze analysis, and movement and gesture analysis. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Firstly, a lack of clear definitions for recognition, classification, detection, and localization tasks hampers the consistent development … Nov 12, 2024 · Advancements in image processing and deep learning offer considerable opportunities for automated defect assessment in civil structures. Finally, we develop a fusion model that integrates the HTLF and HSF, combining surface morphology and skeletal features to improve the precision of SS. Sep 7, 2023 · Download Citation | On Sep 7, 2023, Aruna Kumari V and others published Comparative Analysis Of Deep Learning Models For Human Fall Detection Using Skeleton Features From Video Surveillance System Detecting human falls is a critical task within video surveillance systems, as it can generate an alert to caregivers during the event of a fall, leading to major bone injuries and might cause death. However, most existing works ignore the cross-frame association of skeleton keypoints and aggregation of feature representations. First, we propose a key frame selection technique in a motion sequence of 2D frames based on gradient of optical flow to select the most important frames which characterize different actions. After that, the corresponding frame intervals of heel landing are detected and used for calculating four gait parameters, gait speed Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions Pratik K. The Deep Learning Skeleton Tracker is based on Pose Estimation technology, providing the location of a person in the field of view as well as additional key point metadata on the parts of the body. Higher score indicates better performance on Pixel SkelNetOn dataset, while lower score indicates better performance on Point SkelNetOn dataset. Nov 12, 2024 · Article Mixed Reality-Based Concrete Crack Detection and Skeleton Extraction Using Deep Learning and Image Processing Davood Shojaei * , Peyman Jafary and Zezheng Zhang Centre for Spatial Data Dec 19, 2023 · To address the above situation, we propose a gait detection method based on computer vision for the real-time monitoring of gait during human–machine integrated walking. The system follows a two-stage approach: Keypoint Extraction: Uses MMPOSE to detect and extract human keypoints from video frames Activity Recognition Oct 20, 2025 · We collect existing papers on skeleton-based action recognition published in prominent conferences and journals. Refer to project page for detailed instruction and pretrained models. Nov 1, 2023 · Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with the popularity of deep learning techniques. With the soaring interest in understanding the dynamics of human body skeletons for applications such as action recognition and video understanding, the significance of precise 3D key-point detection has become increasingly prominent. , 2018, Arnab et al. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging. Dec 31, 2022 · In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. In light of these issues, we propose our research method: a deep learning-based human skeletal keypoint detection framework for low-light environments. Our goal is to accurately predict keypoint coordinates corresponding to anatomical landmarks on the human body, such as joints and limbs. Instantly analyze and track skeletal movements with accuracy and ease. In terms of context, skeleton-based approach has the strong advantage of robustness in understanding ac-tual human actions. I. This is the official implementation of our CVPR 2024 paper "BlockGCN: Redefine Topology Awareness for Skeleton-Based Action Recognition" Jul 28, 2025 · Recent advances in deep learning and video-based skeleton extraction have opened new possibilities for accessible, scalable motion assessment using affordable devices such as smartphones or webcams. (2015) have presented a skeleton-based activity recognition system using an end-to-end deep learning model consisting of hierarchical RNNs. The use of videos wi This repository explains deep learning based human action recognition using Skeleton images. All of this demonstrates the diversity of approaches for tasks related to human action recognition. , 2021) to directly use videos as input for deep neural networks. Mar 24, 2021 · As a result, deep learning models are adapted to work in the field of skeleton-based action recognition. The purpose of this study was to help doctors to detect and diagnose fractures more accurately and intuitively, with fewer errors. Firstly, a lack of clear Deep Learning model We built our Deep Learning model refering to Online-Realtime-Action-Recognition-based-on-OpenPose. In some cases, they use pose detection through a human skeleton as a feature extraction method. VCA Deep Learning Skeleton Tracker The Deep Learning Skeleton tracker tracks people in situations where the camera field of view is relatively close. HED [24] learns a pixel-wise classi・‘r to produce edges, which can be also used for skeleton detection. Jan 1, 2024 · In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. Jan 10, 2023 · Bibliographic details on Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions. May 29, 2018 · In this tutorial, Deep Learning based Human Pose Estimation using OpenCV. Although RNN-based approaches present excellent results in 3D action Jul 25, 2023 · This provides better detection or segmentation and direct visualization 19, 20. The The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. What is Human Pose Estimation? Human pose estimation represents a graphical skeleton of a human. The model is implemented in training. (1) This paper is the first work to study the robustness of deep learning-based skeleton detection against adversarial attacks, which are only slightly unlike the original data but still imperceptible to humans. For decades, people have been developing skeleton detection techniques. Jan 30, 2021 · The skeleton is then recovered from the flux representation, which captures the position of skeletal pixels relative to semantically meaningful entities (e. Dec 31, 2022 · The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. Feb 27, 2024 · In recent years, numerous studies have proven that combining 3D skeleton datasets with deep learning has its unique advantages. Mar 15, 2021 · Deep learning architectures can learn hierarchical representation to perform pattern recognition and show impressive results in many pattern recognition tasks. In this project, we aim to develop a human pose estimation model using deep learning techniques. The impact of deep learning has changed the landscape of the vision-based system, such as action recognition. Utilizing “You only look once” (YOLO) v4 AI offers valuable support in fracture detection and diagnostic decision-making. zky hwoe qajxic epes akgxgle homx pdqq lltqbu opqau fhszlih glfq xwy rxsxv ekez bhrrkd