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Journal articles on the topic 'Body Keypoints'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Li, Weiwei, Rong Du, and Shudong Chen. "Semantic–Structural Graph Convolutional Networks for Whole-Body Human Pose Estimation." Information 13, no. 3 (2022): 109. http://dx.doi.org/10.3390/info13030109.

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Existing whole-body human pose estimation methods mostly segment the parts of the body’s hands and feet for specific processing, which not only splits the overall semantics of the body, but also increases the amount of calculation and the complexity of the model. To address these drawbacks, we designed a novel semantic–structural graph convolutional network (SSGCN) for whole-body human pose estimation tasks, which leverages the whole-body graph structure to analyze the semantics of the whole-body keypoints through a graph convolutional network and improves the accuracy of pose estimation. Firs

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Menezes, Guilherme, Ariana Negreiro, Rafael Ferreira, et al. "67 Precision identification and weight assessment of cattle using supervised machine learning on body surface keypoints." Journal of Animal Science 102, Supplement_3 (2024): 310–11. http://dx.doi.org/10.1093/jas/skae234.354.

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Abstract Computer vision systems (CVS) offer identification solutions for animals with distinct coat patterns, but are less effective for solid-colored herds. In addition, they can be used to measure critical phenotypes, such as body weight (BW). Providing both BW and identification for solid-colored animals can help farmers make decisions. This study aimed to 1) develop an automatic CVS capable of identifying using the Euclidean distance between keypoints located at specific anatomical landmarks (e.g., bony prominences), and 2) predict the BW using features extracted from these keypoints. The

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Li, Jia, Wen Su, and Zengfu Wang. "Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11354–61. http://dx.doi.org/10.1609/aaai.v34i07.6797.

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We rethink a well-known bottom-up approach for multi-person pose estimation and propose an improved one. The improved approach surpasses the baseline significantly thanks to (1) an intuitional yet more sensible representation, which we refer to as body parts to encode the connection information between keypoints, (2) an improved stacked hourglass network with attention mechanisms, (3) a novel focal L2 loss which is dedicated to “hard” keypoint and keypoint association (body part) mining, and (4) a robust greedy keypoint assignment algorithm for grouping the detected keypoints into individual p

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Wang, Xilong, Nianfeng Shi, Guoqiang Wang, Jie Shao, and Shuaibo Zhao. "A Multi-Channel Parallel Keypoint Fusion Framework for Human Pose Estimation." Electronics 12, no. 19 (2023): 4019. http://dx.doi.org/10.3390/electronics12194019.

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Although modeling self-attention can significantly reduce computational complexity, human pose estimation performance is still affected by occlusion and background noise, and undifferentiated feature fusion leads to significant information loss. To address these issues, we propose a novel human pose estimation framework called DatPose (deformable convolution and attention for human pose estimation), which combines deformable convolution and self-attention to relieve these issues. Considering that the keypoints of the human body are mostly distributed at the edge of the human body, we adopt the

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Wei, Fan, Guanghua Xu, Qingqiang Wu, Penglin Qin, Leijun Pan, and Yihua Zhao. "Whole-Body 3D Pose Estimation Based on Body Mass Distribution and Center of Gravity Constraints." Sensors 25, no. 13 (2025): 3944. https://doi.org/10.3390/s25133944.

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Estimating the 3D pose of a human body from monocular images is crucial for computer vision applications, but the technique remains challenging due to depth ambiguity and self-occlusion. Traditional methods often suffer from insufficient prior knowledge and weak constraints, resulting in inaccurate 3D keypoint estimation. In this paper, we propose a method for whole-body 3D pose estimation based on a Transformer architecture, integrating body mass distribution and center of gravity constraints. The method maps the pose to the center of gravity position using the anatomical mass ratio of the hu

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Zhang, Jianqiang, Jing Hou, Qiusheng He, Zhengwei Yuan, and Hao Xue. "MambaPose: A Human Pose Estimation Based on Gated Feedforward Network and Mamba." Sensors 24, no. 24 (2024): 8158. https://doi.org/10.3390/s24248158.

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Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation. First, we design a GMamba structure to be used as a backbone network to extract human keypoints. A gating mechanism is introduced into the linear layer of Mamba, which allows the mod

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Peng, Cheng, Shanshan Cao, Shujing Li, Tao Bai, Zengyuan Zhao, and Wei Sun. "Automated Measurement of Cattle Dimensions Using Improved Keypoint Detection Combined with Unilateral Depth Imaging." Animals 14, no. 17 (2024): 2453. http://dx.doi.org/10.3390/ani14172453.

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Traditional measurement methods often rely on manual operations, which are not only inefficient but also cause stress to cattle, affecting animal welfare. Currently, non-contact cattle dimension measurement usually involves the use of multi-view images combined with point cloud or 3D reconstruction technologies, which are costly and less flexible in actual farming environments. To address this, this study proposes an automated cattle dimension measurement method based on an improved keypoint detection model combined with unilateral depth imaging. Firstly, YOLOv8-Pose is selected as the keypoin

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Ahmad, Niaz, Jawad Khan, Jeremy Yuhyun Kim, and Youngmoon Lee. "Joint Human Pose Estimation and Instance Segmentation with PosePlusSeg." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 69–76. http://dx.doi.org/10.1609/aaai.v36i1.19880.

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Despite the advances in multi-person pose estimation, state-of-the-art techniques only deliver the human pose structure.Yet, they do not leverage the keypoints of human pose to deliver whole-body shape information for human instance segmentation. This paper presents PosePlusSeg, a joint model designed for both human pose estimation and instance segmentation. For pose estimation, PosePlusSeg first takes a bottom-up approach to detect the soft and hard keypoints of individuals by producing a strong keypoint heat map, then improves the keypoint detection confidence score by producing a body heat

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Gajic, Dusan, Gorana Gojic, Dinu Dragan, and Veljko Petrovic. "Comparative evaluation of keypoint detectors for 3d digital avatar reconstruction." Facta universitatis - series: Electronics and Energetics 33, no. 3 (2020): 379–94. http://dx.doi.org/10.2298/fuee2003379g.

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Three-dimensional personalized human avatars have been successfully utilized in shopping, entertainment, education, and health applications. However, it is still a challenging task to obtain both a complete and highly detailed avatar automatically. One approach is to use general-purpose, photogrammetry-based algorithms on a series of overlapping images of the person. We argue that the quality of avatar reconstruction can be increased by modifying parts of the photogrammetry-based algorithm pipeline to be more specifically tailored to the human body shape. In this context, we perform an extensi

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Ferres, Kim, Timo Schloesser, and Peter A. Gloor. "Predicting Dog Emotions Based on Posture Analysis Using DeepLabCut." Future Internet 14, no. 4 (2022): 97. http://dx.doi.org/10.3390/fi14040097.

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This paper describes an emotion recognition system for dogs automatically identifying the emotions anger, fear, happiness, and relaxation. It is based on a previously trained machine learning model, which uses automatic pose estimation to differentiate emotional states of canines. Towards that goal, we have compiled a picture library with full body dog pictures featuring 400 images with 100 samples each for the states “Anger”, “Fear”, “Happiness” and “Relaxation”. A new dog keypoint detection model was built using the framework DeepLabCut for animal keypoint detector training. The newly traine

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Desai, Miral, and Hiren Mewada. "A novel approach for yoga pose estimation based on in-depth analysis of human body joint detection accuracy." PeerJ Computer Science 9 (January 13, 2023): e1152. http://dx.doi.org/10.7717/peerj-cs.1152.

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Virtual motion and pose from images and video can be estimated by detecting body joints and their interconnection. The human body has diverse and complicated poses in yoga, making its classification challenging. This study estimates yoga poses from the images using a neural network. Five different yoga poses, viz. downdog, tree, plank, warrior2, and goddess in the form of RGB images are used as the target inputs. The BlazePose model was used to localize the body joints of the yoga poses. It detected a maximum of 33 body joints, referred to as keypoints, covering almost all the body parts. Keyp

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Fu, Dengyu, and Wei Wu. "High-Resolution Representation Learning for Human Pose Estimation based on Transformer." Journal of Physics: Conference Series 2189, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2189/1/012023.

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Abstract Human pose estimation requires accurate coordinate values for the prediction of human joints, which requires a high-resolution representation to effectively improve accuracy. For some difficult joint prediction tasks, it is not only necessary to look at the characteristics of the joint points themselves, but also to make judgments in combination with the context of the whole image. Generally, the resolution will be reduced when the context information is obtained. In this process, it will inevitably lose some spatial information and make the prediction inaccurate. In this paper, we pr

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Lu, Shufang, Funan Lu, Xufeng Shou, and Shuaiyin Zhu. "DeepProfile: Accurate Under-the-Clothes Body Profile Estimation." Applied Sciences 12, no. 4 (2022): 2220. http://dx.doi.org/10.3390/app12042220.

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Accurate human body profiles have many potential applications. Image-based human body profile estimation can be regarded as a fine-grained semantic segmentation problem, which is typically used to locate objects and boundaries in images. However, existing image segmentation methods, such as human parsing, require significant amounts of annotation and their datasets consider clothes as part of the human body profile. Therefore, the results they generate are not accurate when the human subject is dressed in loose-fitting clothing. In this paper, we created and labeled an under-the-clothes human

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Shih, Cheng-Liang, Jun-You Liu, Irin Tri Anggraini, Yanqi Xiao, Nobuo Funabiki, and Chih-Peng Fan. "A Yoga Pose Difficulty Level Estimation Method Using OpenPose for Self-Practice System to Yoga Beginners." Information 15, no. 12 (2024): 789. https://doi.org/10.3390/info15120789.

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Yoga is an exercise preferable for various users at different ages to enhance physical and mental health. To help beginner yoga self-practitioners avoid getting injured by selecting difficult yoga poses, the information of the difficulty level of yoga poses is very important to provide an objective metric to assist yoga self-practitioners in selecting appropriate exercises on the basis of their skill level by using the yoga self-practice system. To enhance the developed yoga self-practice system, the yoga difficulty level estimation function will enable users to clearly understand whether the

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Tang, Wanyu, Chao Shi, Yuanyuan Li, et al. "Keypoints-Based Multi-Cue Feature Fusion Network (MF-Net) for Action Recognition of ADHD Children in TOVA Assessment." Bioengineering 11, no. 12 (2024): 1210. http://dx.doi.org/10.3390/bioengineering11121210.

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Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder among children and adolescents. Behavioral detection and analysis play a crucial role in ADHD diagnosis and assessment by objectively quantifying hyperactivity and impulsivity symptoms. Existing video-based action recognition algorithms focus on object or interpersonal interactions, they may overlook ADHD-specific behaviors. Current keypoints-based algorithms, although effective in attenuating environmental interference, struggle to accurately model the sudden and irregular movements characteristic of AD

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McGuirk, Connor J. C., Natalie Baddour, and Edward D. Lemaire. "Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway." Computation 9, no. 12 (2021): 130. http://dx.doi.org/10.3390/computation9120130.

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New artificial intelligence- (AI) based marker-less motion capture models provide a basis for quantitative movement analysis within healthcare and eldercare institutions, increasing clinician access to quantitative movement data and improving decision making. This research modelled, simulated, designed, and implemented a novel marker-less AI motion-analysis approach for institutional hallways, a Smart Hallway. Computer simulations were used to develop a system configuration with four ceiling-mounted cameras. After implementing camera synchronization and calibration methods, OpenPose was used t

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Ko, Sang-Ki, Chang Jo Kim, Hyedong Jung, and Choongsang Cho. "Neural Sign Language Translation Based on Human Keypoint Estimation." Applied Sciences 9, no. 13 (2019): 2683. http://dx.doi.org/10.3390/app9132683.

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We propose a sign language translation system based on human keypoint estimation. It is well-known that many problems in the field of computer vision require a massive dataset to train deep neural network models. The situation is even worse when it comes to the sign language translation problem as it is far more difficult to collect high-quality training data. In this paper, we introduce the KETI (Korea Electronics Technology Institute) sign language dataset, which consists of 14,672 videos of high resolution and quality. Considering the fact that each country has a different and unique sign l

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Li, Yaowei, Fei Guo, Miaotian Zhang, et al. "A Novel Deep Learning-Based Pose Estimation Method for Robotic Grasping of Axisymmetric Bodies in Industrial Stacked Scenarios." Machines 10, no. 12 (2022): 1141. http://dx.doi.org/10.3390/machines10121141.

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A vision-based intelligent robotic grasping system is essential for realizing unmanned operations in industrial manufacturing, and pose estimation plays an import role in this system. In this study, deep learning was used to obtain the 6D pose of an axisymmetric body which was optimal for robotic grasping in industrial stacked scenarios. We propose a method to obtain the 6D pose of an axisymmetric body by detecting the pre-defined keypoints on the side surface. To realize this method and solve other challenges in industrial stacked scenarios, we propose a multitask real-time convolutional neur

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Thành, Nguyễn Tường, Lê Văn Hùng, and Phạm Thành Công. "An Evaluation of Pose Estimation in Video of Traditional Martial Arts Presentation." Journal of Research and Development on Information and Communication Technology 2019, no. 2 (2019): 114–26. http://dx.doi.org/10.32913/mic-ict-research.v2019.n2.864.

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Preserving, maintaining, and teaching traditional martial arts are very important activities in social life. That helps individuals preserve national culture, exercise, and practice self-defense. However, traditional martial arts have many differentposturesaswellasvariedmovementsofthebodyand body parts. The problem of estimating the actions of human body still has many challenges, such as accuracy, obscurity, and so forth. This paper begins with a review of several methods of 2-D human pose estimation on the RGB images, in which the methods of using the Convolutional Neural Network (CNN) model

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Pedersen, Malte, Marianne Nyegaard, and Thomas B. Moeslund. "Finding Nemo’s Giant Cousin: Keypoint Matching for Robust Re-Identification of Giant Sunfish." Journal of Marine Science and Engineering 11, no. 5 (2023): 889. http://dx.doi.org/10.3390/jmse11050889.

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The Giant Sunfish (Mola alexandrini) has unique patterns on its body, which allow for individual identification. By continuously gathering and matching images, it is possible to monitor and track individuals across location and time. However, matching images manually is a tedious and time-consuming task. To automate the process, we propose a pipeline based on finding and matching keypoints between image pairs. We evaluate our pipeline with four different keypoint descriptors, namely ORB, SIFT, RootSIFT, and SuperPoint, and demonstrate that the number of matching keypoints between a pair of ima

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Artacho, Bruno, and Andreas Savakis. "Full-BAPose: Bottom Up Framework for Full Body Pose Estimation." Sensors 23, no. 7 (2023): 3725. http://dx.doi.org/10.3390/s23073725.

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We present Full-BAPose, a novel bottom-up approach for full body pose estimation that achieves state-of-the-art results without relying on external people detectors. The Full-BAPose method addresses the broader task of full body pose estimation including hands, feet, and facial landmarks. Our deep learning architecture is end-to-end trainable based on an encoder-decoder configuration with HRNet backbone and multi-scale representations using a disentangled waterfall atrous spatial pooling module. The disentangled waterfall module leverages the efficiency of progressive filtering, while maintain

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Zhang, Juze, Ye Shi, Yuexin Ma, Lan Xu, Jingyi Yu, and Jingya Wang. "IKOL: Inverse Kinematics Optimization Layer for 3D Human Pose and Shape Estimation via Gauss-Newton Differentiation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3454–62. http://dx.doi.org/10.1609/aaai.v37i3.25454.

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This paper presents an inverse kinematic optimization layer (IKOL) for 3D human pose and shape estimation that leverages the strength of both optimization- and regression-based methods within an end-to-end framework. IKOL involves a nonconvex optimization that establishes an implicit mapping from an image’s 3D keypoints and body shapes to the relative body-part rotations. The 3D keypoints and the body shapes are the inputs and the relative body-part rotations are the solutions. However, this procedure is implicit and hard to make differentiable. So, to overcome this issue, we designed a Gauss-

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Lim, Josiah Wey Tsen, Connie Tee, and Michael Kah Ong Goh. "Exploring Activities of Daily Living Among the Elderly through Machine Learning Techniques." International Journal on Robotics, Automation and Sciences 7, no. 1 (2025): 35–46. https://doi.org/10.33093/ijoras.2025.7.1.5.

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Activities of daily living (ADLs) is a term that is used to describe the activities performed in everyday life that involves the motion of the human body such as eating, walking, and sitting. ADLs can be used to determine the state of elderly people as a decline in ADL performance will generally mean a decline in the human body. It can act as an early indicator if an elderly person is experiencing underlying illness or health issue. This project aims to detect five different ADLs which are eating, cooking, sweeping, walking, and sitting and standing. A dataset was collected from twenty individ

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Anitha.J, D. Dhivya., Harishma.S, and Mythili.N. "Smart Body Posture Guidance System." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 1264–66. https://doi.org/10.35940/ijeat.E1168.069520.

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Body posture plays a crucial role in the measurement of body vitals. Accurate measurement of blood pressure, body weight, etc. requires correct posture which greatly affects the result. To achieve the correct posture the person has to depend on the lab technician, who helps the person to sit in the correct pose, to place the cuff in the right place to measure blood pressure, to stand in the correct pose to measure body weight, etc. To eliminate the need for lab technician, we develop a system, to capture image and recognize the pose using a camera, image processing using pose estimation algori

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Favorskaya, Margarita N., and Dmitriy N. Natalenko. "Semantically-Based Animal Pose Estimation in the Wild." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W5-2024 (December 16, 2024): 33–40. https://doi.org/10.5194/isprs-archives-xlviii-2-w5-2024-33-2024.

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Abstract. Accurate animal pose estimation in the wild is potentially useful for many downstream applications such as wildlife conservation. Currently, the main approach to assessing animal poses is based on identifying keypoints of the body and constructing the skeleton. However, a direct application of frameworks to human pose estimation is not successful due to the features of the skeletal structure of humans and mammals. In this study, we propose a two-stage method: coarse-tuning with animal detection using a bounding box, as is done in most similar methods, and fine-tuning with semantic se

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Xiao, Yabo, Xiaojuan Wang, Mingshu He, Lei Jin, Mei Song, and Jian Zhao. "A Compact and Powerful Single-Stage Network for Multi-Person Pose Estimation." Electronics 12, no. 4 (2023): 857. http://dx.doi.org/10.3390/electronics12040857.

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Multi-person pose estimation generally follows top-down and bottom-up paradigms. The top-down paradigm detects all human boxes and then performs single-person pose estimation on each ROI. The bottom-up paradigm locates identity-free keypoints and then groups them into individuals. Both of them use an extra stage to build the relationship between human instance and corresponding keypoints (e.g., human detection in a top-down manner or a grouping process in a bottom-up manner). The extra stage leads to a high computation cost and a redundant two-stage pipeline. To address the above issue, we int

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Zwölfer, Michael, Martin Mössner, Helge Rhodin, Werner Nachbauer, and Dieter Heinrich. "Integration of a skier-specific keypoint detection model in a hybrid 3D motion capture pipeline." Current Issues in Sport Science (CISS) 9, no. 4 (2024): 013. http://dx.doi.org/10.36950/2024.4ciss013.

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Introduction & Purpose Alpine skiing, like many outdoor sports, presents significant challenges for motion capture due to its large capture volumes, high athlete speeds, variable environmental conditions, and occlusions, e.g., due to snow spray. While traditional marker-based motion capture systems offer highest precision in the lab, they are usually unsuitable for outdoor settings. Sensor-based methods, such as inertial measurement units, however, may suffer from inaccuracies due to sensor noise and drift, while they only provide relative segment positions (Fasel et al., 2018). Therefore,

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Yousif, Khalid, Alireza Bab-Hadiashar, and Reza Hoseinnezhad. "3D SLAM in texture-less environments using rank order statistics." Robotica 35, no. 4 (2015): 809–31. http://dx.doi.org/10.1017/s0263574715000831.

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SUMMARYWe present a real time 3D SLAM system for texture-less scenes using only depth information provided by a low cost RGB-D sensor. The proposed method is based on a novel informative sampling scheme that extracts points carrying the most useful 3D information for registration. The aim of the proposed sampling technique is to informatively sample a point cloud into a subset of points based on their 3D information. The flatness of a point is measured by applying a rank order statistics based robust segmentation method to surface normals in its local vicinity. The extracted keypoints from seq

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Samaan, Gerges H., Abanoub R. Wadie, Abanoub K. Attia, et al. "MediaPipe’s Landmarks with RNN for Dynamic Sign Language Recognition." Electronics 11, no. 19 (2022): 3228. http://dx.doi.org/10.3390/electronics11193228.

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Communication for hearing-impaired communities is an exceedingly challenging task, which is why dynamic sign language was developed. Hand gestures and body movements are used to represent vocabulary in dynamic sign language. However, dynamic sign language faces some challenges, such as recognizing complicated hand gestures and low recognition accuracy, in addition to each vocabulary’s reliance on a series of frames. This paper used MediaPipe in conjunction with RNN models to address dynamic sign language recognition issues. MediaPipe was used to determine the location, shape, and orientation b

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Inturi, Anitha Rani, Vazhora Malayil Manikandan, Mahamkali Naveen Kumar, Shuihua Wang, and Yudong Zhang. "Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection." Sensors 23, no. 14 (2023): 6283. http://dx.doi.org/10.3390/s23146283.

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According to the World Health Organisation, falling is a major health problem with potentially fatal implications. Each year, thousands of people die as a result of falls, with seniors making up 80% of these fatalities. The automatic detection of falls may reduce the severity of the consequences. Our study focuses on developing a vision-based fall detection system. Our work proposes a new feature descriptor that results in a new fall detection framework. The body geometry of the subject is analyzed and patterns that help to distinguish falls from non-fall activities are identified in our propo

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Rajarathinam, Robin Jephthah, Chris Palaguachi, and Jina Kang. "360-Degree Cameras vs Traditional Cameras in Multimodal Learning Analytics: Comparative Study of Facial Recognition and Pose Estimation." Journal of Educational Data Mining 17, no. 1 (2025): 157–82. https://doi.org/10.5281/zenodo.14966499.

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Multimodal Learning Analytics (MMLA) has emerged as a powerful approach within the computer-supported collaborative learning community, offering nuanced insights into learning processes through diverse data sources. Despite its potential, the prevalent reliance on traditional instruments such as tripod-mounted digital cameras for video capture often results in suboptimal data quality for facial expressions and poses captured, which is crucial for understanding collaborative dynamics. This study introduces an innovative approach to overcome this limitation by employing 360-degree camera technol

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Samba Siva Rao Kopanaty, Siva Venkata Sai Emani, Uday Kumar Cherukuri, and Manoj Duggirala. "Human Pose Tracking with MoveNet." international journal of engineering technology and management sciences 9, no. 2 (2025): 466–75. https://doi.org/10.46647/ijetms.2025.v09i02.058.

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In computer vision, human pose tracking is an application in human-computer interaction, sports analysis, and healthcare. It explores the application of Google’s MoveNet, a light weight stateof-the-art CNN model for real time human pose estimation in images and videos streams. MoveNet detects the multiple keypoints in the human body efficiently and enables the posture and movement analysis. It demonstrates the model’s ability to identify and track the key body joints effectively and highlighting its potential in applications like fitness tracking, human-computer interaction and activity recogn

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Borgmann, B., M. Hebel, M. Arens, and U. Stilla. "INFORMATION ACQUISITION ON PEDESTRIAN MOVEMENTS IN URBAN TRAFFIC WITH A MOBILE MULTI-SENSOR SYSTEM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 131–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-131-2021.

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Abstract. This paper presents an approach which combines LiDAR sensors and cameras of a mobile multi-sensor system to obtain information about pedestrians in the vicinity of the sensor platform. Such information can be used, for example, in the context of driver assistance systems. In the first step, our approach starts by using LiDAR sensor data to detect and track pedestrians, benefiting from LiDAR’s capability to directly provide accurate 3D data. After LiDAR-based detection, the approach leverages the typically higher data density provided by 2D cameras to determine the body pose of the de

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Sarvesh Kumar. "Precision-Driven Real-Time Pose Estimation for Therapeutic Interventions: Advanced Heatmap Regression, Reference Video Alignment, and Real-Time Corrective Feedback." Journal of Information Systems Engineering and Management 10, no. 34s (2025): 299–310. https://doi.org/10.52783/jisem.v10i34s.5802.

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Accurate movement and posture are essential for effective physical therapy, as improper form can hinder recovery and worsen injuries. This project introduces a real-time human pose estimation system specifically designed for physical therapy, providing precise feedback on body alignment. Utilizing a mod- ified YOLOv8 architecture with custom heatmap regression, the system monitors key joints—particularly the wrist, elbow, and shoulder—vital for upper-body rehabilitation. Initially trained on a combined MPII and COCO 2017 dataset, the model was fine-tuned on a custom dataset of 6,000 images der

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Wang, Jue, and Zhigang Luo. "Pointless Pose: Part Affinity Field-Based 3D Pose Estimation without Detecting Keypoints." Electronics 10, no. 8 (2021): 929. http://dx.doi.org/10.3390/electronics10080929.

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Human pose estimation finds its application in an extremely wide domain and is therefore never pointless. We propose in this paper a new approach that, unlike any prior one that we are aware of, bypasses the 2D keypoint detection step based on which the 3D pose is estimated, and is thus pointless. Our motivation is rather straightforward: 2D keypoint detection is vulnerable to occlusions and out-of-image absences, in which case the 2D errors propagate to 3D recovery and deteriorate the results. To this end, we resort to explicitly estimating the human body regions of interest (ROI) and their 3

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Cheng, Yu, Bo Yang, Bo Wang, and Robby T. Tan. "3D Human Pose Estimation Using Spatio-Temporal Networks with Explicit Occlusion Training." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10631–38. http://dx.doi.org/10.1609/aaai.v34i07.6689.

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Estimating 3D poses from a monocular video is still a challenging task, despite the significant progress that has been made in the recent years. Generally, the performance of existing methods drops when the target person is too small/large, or the motion is too fast/slow relative to the scale and speed of the training data. Moreover, to our knowledge, many of these methods are not designed or trained under severe occlusion explicitly, making their performance on handling occlusion compromised. Addressing these problems, we introduce a spatio-temporal network for robust 3D human pose estimation

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Jiao, Yingying, Zhigang Wang, Zhenguang Liu, et al. "Optimizing Human Pose Estimation Through Focused Human and Joint Regions." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 4102–10. https://doi.org/10.1609/aaai.v39i4.32430.

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Human pose estimation has given rise to a broad spectrum of novel and compelling applications, including action recognition, sports analysis, as well as surveillance. However, accurate video pose estimation remains an open challenge. One aspect that has been overlooked so far is that existing methods learn motion clues from all pixels rather than focusing on the target human body, making them easily misled and disrupted by unimportant information such as background changes or movements of other people. Additionally, while the current Transformer-based pose estimation methods has demonstrated i

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Kim, Jeongwon. "A Posture Similarity Analysis Scheme based on the Confidence Score of Human Body Keypoints." Journal of Korean Institute of Information Technology 21, no. 12 (2023): 171–77. http://dx.doi.org/10.14801/jkiit.2023.21.12.171.

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Meng, Xianjia, Yong Yang, Kang Li, and Zuobin Ying. "A Structure-Aware Adversarial Framework with the Keypoint Biorientation Field for Multiperson Pose Estimation." Wireless Communications and Mobile Computing 2022 (February 14, 2022): 1–17. http://dx.doi.org/10.1155/2022/3447827.

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Human pose estimation is aimed at locating the anatomical parts or keypoints of the human body and is regarded as a core component in obtaining detailed human understanding in images or videos. However, the occlusion and overlap upon human bodies and complex backgrounds often result in implausible pose predictions. To address the problem, we propose a structure-aware adversarial framework, which combines cues of local joint interconnectivity and priors about the holistic structure of human bodies, achieving high-quality results for multiperson human pose estimation. Effective learning of such

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Dorea, Joao, and Guilherme Lobato Menezes. "462 Artificial intelligence and machine learning to improve livestock farming." Journal of Animal Science 102, Supplement_3 (2024): 296. http://dx.doi.org/10.1093/jas/skae234.338.

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Abstract One of the constraints on advancing livestock research is the lack of large-scale quantification of physiological traits, a process often constrained by financial, labor-intensive, and scalability limitations, in addition to being traditionally performed through invasive methods. However, in the era of big data, the collection of large, extensive, and heterogeneous datasets via digital technologies, coupled with Artificial Intelligence (AI) techniques, has catalyzed significant progress across various research domains, including precision medicine, education, finance, and environmenta

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Ye, Run Zhou, Arun Subramanian, Daniel Diedrich, Heidi Lindroth, Brian Pickering, and Vitaly Herasevich. "Effects of Image Quality on the Accuracy Human Pose Estimation and Detection of Eye Lid Opening/Closing Using Openpose and DLib." Journal of Imaging 8, no. 12 (2022): 330. http://dx.doi.org/10.3390/jimaging8120330.

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Objective: The application of computer models in continuous patient activity monitoring using video cameras is complicated by the capture of images of varying qualities due to poor lighting conditions and lower image resolutions. Insufficient literature has assessed the effects of image resolution, color depth, noise level, and low light on the inference of eye opening and closing and body landmarks from digital images. Method: This study systematically assessed the effects of varying image resolutions (from 100 × 100 pixels to 20 × 20 pixels at an interval of 10 pixels), lighting conditions (

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Jiang, Yi, Kexin Yang, Jinlin Zhu, and Li Qin. "YOLO-Rlepose: Improved YOLO Based on Swin Transformer and Rle-Oks Loss for Multi-Person Pose Estimation." Electronics 13, no. 3 (2024): 563. http://dx.doi.org/10.3390/electronics13030563.

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In recent years, there has been significant progress in human pose estimation, fueled by the widespread adoption of deep convolutional neural networks. However, despite these advancements, multi-person 2D pose estimation still remains highly challenging due to factors such as occlusion, noise, and non-rigid body movements. Currently, most multi-person pose estimation approaches handle joint localization and association separately. This study proposes a direct regression-based method to estimate the 2D human pose from a single image. The authors name this network YOLO-Rlepose. Compared to tradi

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Pham, Huy Hieu, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Sergio A. Velastin, and Pablo Zegers. "A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera." Sensors 20, no. 7 (2020): 1825. http://dx.doi.org/10.3390/s20071825.

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from RGB sensors using simple cameras. The approach proceeds along two stages. In the first, a real-time 2D pose detector is run to determine the precise pixel location of important keypoints of the human body. A two-stream deep neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second stage, the Efficient Neural Architecture Search (ENAS) algorithm is deployed to find an optimal network architecture that is used for modeling the spatio-temp

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Lu, Tianyi, Ke Cheng, Xuecheng Hua, and Suning Qin. "KSL-POSE: A Real-Time 2D Human Pose Estimation Method Based on Modified YOLOv8-Pose Framework." Sensors 24, no. 19 (2024): 6249. http://dx.doi.org/10.3390/s24196249.

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Two-dimensional human pose estimation aims to equip computers with the ability to accurately recognize human keypoints and comprehend their spatial contexts within media content. However, the accuracy of real-time human pose estimation diminishes when processing images with occluded body parts or overlapped individuals. To address these issues, we propose a method based on the YOLO framework. We integrate the convolutional concepts of Kolmogorov–Arnold Networks (KANs) through introducing non-linear activation functions to enhance the feature extraction capabilities of the convolutional kernels

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Nocera, Antonio, Linda Senigagliesi, Gianluca Ciattaglia, Michela Raimondi, and Ennio Gambi. "ML-Based Edge Node for Monitoring Peoples’ Frailty Status." Sensors 24, no. 13 (2024): 4386. http://dx.doi.org/10.3390/s24134386.

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The development of contactless methods to assess the degree of personal hygiene in elderly people is crucial for detecting frailty and providing early intervention to prevent complete loss of autonomy, cognitive impairment, and hospitalisation. The unobtrusive nature of the technology is essential in the context of maintaining good quality of life. The use of cameras and edge computing with sensors provides a way of monitoring subjects without interrupting their normal routines, and has the advantages of local data processing and improved privacy. This work describes the development an intelli

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Rao, Romesa, Salman Qadri, and Rao Kashif. "Realtime Monitoring of Animal Behavior Using Deep Learning Models." Journal of Agriculture and Environment for International Development (JAEID) 119, no. 1 (2025): 125–48. https://doi.org/10.36253/jaeid-16397.

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Accurate monitoring of animal health and behavior is crucial for improving welfare and productivity in livestock management. Traditional observation methods are time-consuming and prone to subjective bias. To address these challenges, we propose an automated system for behavioral pattern using deep learning-based pose estimation techniques. Specifically, we utilize ResNet-50, a deep convolutional neural network, to detect key anatomical landmarks such as the nose, eyes, ears, and body center. By tracking these keypoints, we generate movement trajectories that help identify behavioral patterns.

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Kongshi, Manqi, Daohua Pan, and Minglong Wang. "Enhancing Dance Performance for Body Motion Interaction Through Swarm Intelligence and Deep Learning." International Journal of Swarm Intelligence Research 16, no. 1 (2025): 1–18. https://doi.org/10.4018/ijsir.383942.

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This paper proposes a novel framework to combine deep learning-based pose estimation with Swarm Intelligence (SI) for real-time stage optimization. In details, a multi-stage convolutional neural network is first employed to extract accurate skeletal keypoints from live video feeds of dance performances, while temporal smoothing techniques mitigate noise and occlusion in complex choreographic scenarios. These pose data are then used by an SI optimizer to iteratively refine stage parameters—lighting color, brightness, projection content—under stringent low-latency requirements. By formulating an

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Saeed, Sohaib Mustafa, Hassan Akbar, Tahir Nawaz, Hassan Elahi, and Umar Shahbaz Khan. "Body-Pose-Guided Action Recognition with Convolutional Long Short-Term Memory (LSTM) in Aerial Videos." Applied Sciences 13, no. 16 (2023): 9384. http://dx.doi.org/10.3390/app13169384.

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The accurate detection and recognition of human actions play a pivotal role in aerial surveillance, enabling the identification of potential threats and suspicious behavior. Several approaches have been presented to address this problem, but the limitation still remains in devising an accurate and robust solution. To this end, this paper presents an effective action recognition framework for aerial surveillance, employing the YOLOv8-Pose keypoints extraction algorithm and a customized sequential ConvLSTM (Convolutional Long Short-Term Memory) model for classifying the action. We performed a de

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Xu, Zhankang, Qifeng Li, Weihong Ma, et al. "A geodesic distance regression-based semantic keypoints detection method for pig point clouds and body size measurement." Computers and Electronics in Agriculture 234 (July 2025): 110285. https://doi.org/10.1016/j.compag.2025.110285.

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Chaudhari, Mr H. P. "Virtual Fitness Trainer Using AI: A Research Paper." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 4082–89. http://dx.doi.org/10.22214/ijraset.2024.62505.

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Abstract: Our research delves into the creation of a Virtual Fitness Trainer using Artificial Intelligence (AI) to offer personalized workout guidance and real-time feedback. This project harnesses advanced computer vision, machine learning, and natural language processing techniques to develop an intelligent system capable of accurately detecting human poses, tracking exercise repetitions, and providing corrective feedback. Utilizing Python's OpenCV library to capture live webcam feeds, processed by MediaPipe's BlazePose tool for precise pose estimation, our application employs a novel topolo

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