Relevant bibliographies by topics / Body Keypoints

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Body Keypoints'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Body Keypoints"

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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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Dissertations / Theses on the topic "Body Keypoints"

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Caha, Miloš. "Určení směru pohledu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237168.

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Main object of this work is to design and implement the algorithm for look direction determination, respectively the head movement. More specifically, it is a system that searches face in the video and then detects points, suitable for view direction estimation of tracked person. Estimation is realized using searching transformation, which has been performed on key points during head movement. For accuracy enhancement the calibration frames are used. Calibration frames determines the key points transformation in defined view directions. Main result is an application able to determine deflectio
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Sládeček, Martin. "3D rekonstrukce z více pohledů kamer." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400663.

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This thesis deals with the task of three-dimensional scene reconstruction using image data obtained from multiple views. It is assumed that intrinsic parameters of the utilized cameras are known. The theoretical chapters describe the basic priciples of individual reconstruction steps. Variuous possible implementaions of data model suitable for this task are also described. The practical part also includes a comparison of false keypoint correspondence filtering, implementation of polar stereo rectification and comparison of disparity map calculation methods that are bundled with the OpenCV libr
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Urban, Daniel. "Lokalizace mobilního robota v prostředí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385923.

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This diploma thesis deals with the problem of mobile robot localisation in the environment based on current 2D and 3D sensor data and previous records. Work is focused on detecting previously visited places by robot. The implemented system is suitable for loop detection, using the Gestalt 3D descriptors. The output of the system provides corresponding positions on which the robot was already located. The functionality of the system has been tested and evaluated on LiDAR data.
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Jelínek, Ondřej. "Podobnost obrazů na základě bodů zájmu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-220409.

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This paper presents a new object detection method. The method is based on keypoints analysis and their parameters. Computed parameters are used for building a decision model using machine learning methods. The model is able to detect object in the picture based on input data and compares its similarity to the chosen example. The new method is described in detail, its accuracy is evaluated and this accuracy is compared to other existing detectors. The new method’s detection ability is by more than 40% better than detection ability of detectors like SURF. In order to understand the object detect
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Bartončík, Michal. "Rozpoznávání výrazu tváře u neznámých osob." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219322.

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This paper describes the various components and phases of the search and recognition of facial expressions of unknown persons. They are presented here as well as possible solutions and methods of addressing each phase of the project. My master’s thesis is designed to recognize facial expressions of unknown persons. For this thesis, I was lent industrial video camera, computer, and place in a laboratory. Furthermore, we introduce the color spaces and their use. From the lead representatives selects the most appropriate assistance for the use of Matlab and the proposed algorithm. After finding a
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Šimetka, Vojtěch. "3D Rekonstrukce historických míst z obrázků na Flickru." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234976.

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Tato práce popisuje problematiku návrhu a vývoje aplikace pro rekonstrukci 3D modelů z 2D obrazových dat, označované jako bundle adjustment. Práce analyzuje proces 3D rekonstrukce a důkladně popisuje jednotlivé kroky. Prvním z kroků je automatizované získání obrazové sady z internetu. Je představena sada skriptů pro hromadné stahování obrázků ze služeb Flickr a Google Images a shrnuty požadavky na tyto obrázky pro co nejlepší 3D rekonstrukci. Práce dále popisuje různé detektory, extraktory a párovací algoritmy klíčových bodů v obraze s cílem najít nejvhodnější kombinaci pro rekonstrukci budov.
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Kuzdas, Oldřich. "Skládání snímků panoramatického pohledu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217435.

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This paper deals step by step with process of stitching images taken by perspective camera rotated by its optical center into the panoramic image. There are described keypoint searching algorhytms, possibilities of calculating homography matrix and methods of eliminating unwanted seams between source images in final panoramic image. A part of this paper is also standalone application in which are implemented some algorhytms described in the work.
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Labudová, Kristýna. "Rozpoznávání obrazů pro ovládání robotické ruky." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316835.

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This thesis concerns with processing of embedded terminals’ images and their classification. There is problematics of moire noise reduction thought filtration in frequency domain and the image normalization for further processing analyzed. Keypoints detectors and descriptors are used for image classification. Detectors FAST and Harris corner detector and descriptors SURF, BRIEF and BRISK are emphasized as well as their evaluation in terms of potential contribution to this work.
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Book chapters on the topic "Body Keypoints"

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Schneider, Pascal, Raphael Memmesheimer, Ivanna Kramer, and Dietrich Paulus. "Gesture Recognition in RGB Videos Using Human Body Keypoints and Dynamic Time Warping." In RoboCup 2019: Robot World Cup XXIII. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35699-6_22.

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Xiong, Wenlu, and Zengbo Xu. "Real-Time Clothing Virtual Display Based on Human Pose Estimation." In Artificial Intelligence and Human-Computer Interaction. IOS Press, 2024. http://dx.doi.org/10.3233/faia240168.

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This paper explores clothing virtual display technology based on human pose estimation and applies it to the Unity3D platform. This technology combines human pose estimation and virtual character-driven approaches to achieve rapid real-time clothing try-on and display effects. Firstly, key point detection of the human body in input images or videos is performed using human pose estimation techniques. This accurately infers the positions and connectivity relationships of human body keypoints, capturing subtle changes in human pose. Kalman filtering techniques are applied to enhance the stabilit
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Kanimozhi S, Anbarasi S, and Mythili M. "Distinct Actions Classification Using Human Action Tracker Technique in Sports Videos." In Advances in Parallel Computing Technologies and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/apc210142.

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Recognizing human action in sports is difficult task as various sequences of activities involved in every scene. Identifying each action individually without overlapping of movements is a tedious process due to continuous change of frames within short duration. So proper tracking of human movements for each action is important. Hence new structure-based human action recognition and tracker technique (HART) is proposed. It uses joint trajectory images and visual feature to design each human action. At first, a structural based method employed to extract human skeleton data points from RGB (Red
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Hoang, Minh Long. "Human Pose Estimation for Rehabilitation by Computer Vision." In Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application. BENTHAM SCIENCE PUBLISHERS, 2024. https://doi.org/10.2174/9789815313055124010008.

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Human pose estimation (HPE) is a valuable tool for rehabilitation, providing critical insights into the body's posture and movements. Both patients and therapists can significantly benefit from this technology, which enhances various aspects of the rehabilitation process by offering precise and real-time feedback on body mechanics. This research explores four well-known models in HPE: BlazePose, OpenPose, MoveNet, and OpenPifPaf. Each model is examined in detail, focusing on their architecture and working principles. BlazePose is renowned for its efficiency and accuracy, making it suitable for
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Conference papers on the topic "Body Keypoints"

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Aljawaheri, Dalia, Gertjan J. Burghouts, Jan Erik van Woerden, Judith Dijk, and Hugo J. Kuijf. "Zero-shot neuro-symbolic parsing of body keypoints." In Electro-optical and Infrared Systems: Technology and Applications XXI, edited by Duncan L. Hickman, Helge Bürsing, Philip J. Soan, and Ove Steinvall. SPIE, 2024. http://dx.doi.org/10.1117/12.3031487.

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Shih, Cheng-Liang, Jun-You Liu, Irin Tri Anggraini, Yanqi Xiao, Nobuo Funabiki, and Chih-Peng Fan. "Difficulty Analyses of Yoga Poses with Center of Gravity Distribution of Body Area by OpenPose Keypoints Detection." In 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2024. http://dx.doi.org/10.1109/icce-taiwan62264.2024.10674448.

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Wang, Ling, Xue Yan, Haoyu Hao, and Tie Hua Zhou. "Multi-Features Fusion Scene Classification Model Based on Body Skeleton and Facial Keypoints Recognition Over Large-Scale Crowd Videos." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033655.

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Shih, Cheng-Liang, Jun-You Liu, Irin Tri Anggraini, Yanqi Xiao, Nobuo Funabiki, and Chih-Peng Fan. "OpenPose Based Poses Difficulty Assessment by 6-Keypoint Body Area for Yoga Self-Practice System." In 2024 5th International Conference on Information Technology and Education Technology (ITET). IEEE, 2024. http://dx.doi.org/10.1109/itet64267.2024.00020.

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Wen, Bo. "Language based object detection with human body using CLIPseg : Integrating CLIP and Optical Flow for Unsupervised Object Tracking and Keypoint Detection." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10957932.

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Liu, Weizhen, Jiayu Tan, Guangyu Lan, et al. "Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection - Towards Precise Fish Morphological Assessment in Aquaculture Breeding." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/816.

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Accurate phenotypic analysis in aquaculture breeding necessitates the quantification of subtle morphological phenotypes. Existing datasets suffer from limitations such as small scale, limited species coverage, and inadequate annotation of keypoints for measuring refined and complex morphological phenotypes of fish body parts. To address this gap, we introduce FishPhenoKey, a comprehensive dataset comprising 23,331 high-resolution images spanning six fish species. Notably, FishPhenoKey includes 22 phenotype-oriented annotations, enabling the capture of intricate morphological phenotypes. Motiva
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Qian, Lihui, Xintong Han, Faqiang Wang, et al. "XFormer: Fast and Accurate Monocular 3D Body Capture." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/148.

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We present XFormer, a novel human mesh and motion capture method that achieves real-time performance on consumer CPUs given only monocular images as input. The proposed network architecture contains two branches: a keypoint branch that estimates 3D human mesh vertices given 2D keypoints, and an image branch that makes prediction directly from the RGB image features. At the core of our method is a cross-modal transformer block that allows information flow across these two branches by modeling the attention between 2D keypoint coordinates and image spatial features. Our architecture is smartly d
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Hua, Minjie, Yibing Nan, and Shiguo Lian. "Falls Prediction Based on Body Keypoints and Seq2Seq Architecture." In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00158.

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Ludwig, Katja, Julian Lorenz, Robin Schön, and Rainer Lienhart. "All Keypoints You Need: Detecting Arbitrary Keypoints on the Body of Triple, High, and Long Jump Athletes." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2023. http://dx.doi.org/10.1109/cvprw59228.2023.00546.

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Lakis, Rachad, Joseph Constantin, Ibtissam Constantin, Vinh Truong Hoang, and Yassine Ruichek. "Pose Estimation Keypoints in Age Recognition of Full Body Image." In 2023 IEEE 4th International Multidisciplinary Conference on Engineering Technology (IMCET). IEEE, 2023. http://dx.doi.org/10.1109/imcet59736.2023.10368248.

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