Relevant bibliographies by topics / Human keypoint detection

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

Academic literature on the topic 'Human keypoint detection'

Author: Grafiati
Published: 25 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Human keypoint detection"

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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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Mundt, Marion, Zachery Born, Molly Goldacre, and Jacqueline Alderson. "Estimating Ground Reaction Forces from Two-Dimensional Pose Data: A Biomechanics-Based Comparison of AlphaPose, BlazePose, and OpenPose." Sensors 23, no. 1 (2022): 78. http://dx.doi.org/10.3390/s23010078.

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The adoption of computer vision pose estimation approaches, used to identify keypoint locations which are intended to reflect the necessary anatomical landmarks relied upon by biomechanists for musculoskeletal modelling, has gained increasing traction in recent years. This uptake has been further accelerated by keypoint use as inputs into machine learning models used to estimate biomechanical parameters such as ground reaction forces (GRFs) in the absence of instrumentation required for direct measurement. This study first aimed to investigate the keypoint detection rate of three open-source p
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Li, Zhen, Yuliang Gao, Qingqing Hong, Yuren Du, Seiichi Serikawa, and Lifeng Zhang. "Keypoint3D: Keypoint-Based and Anchor-Free 3D Object Detection for Autonomous Driving with Monocular Vision." Remote Sensing 15, no. 5 (2023): 1210. http://dx.doi.org/10.3390/rs15051210.

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Autonomous driving has received enormous attention from the academic and industrial communities. However, achieving full driving autonomy is not a trivial task, because of the complex and dynamic driving environment. Perception ability is a tough challenge for autonomous driving, while 3D object detection serves as a breakthrough for providing precise and dependable 3D geometric information. Inspired by practical driving experiences of human experts, a pure visual scheme takes sufficient responsibility for safe and stable autonomous driving. In this paper, we proposed an anchor-free and keypoi
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Zhang, Jing, Zhe Chen, and Dacheng Tao. "Towards High Performance Human Keypoint Detection." International Journal of Computer Vision 129, no. 9 (2021): 2639–62. http://dx.doi.org/10.1007/s11263-021-01482-8.

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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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Aidoo, Evans, Xun Wang, Zhenguang Liu, et al. "Cofopose: Conditional 2D Pose Estimation with Transformers." Sensors 22, no. 18 (2022): 6821. http://dx.doi.org/10.3390/s22186821.

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Human pose estimation has long been a fundamental problem in computer vision and artificial intelligence. Prominent among the 2D human pose estimation (HPE) methods are the regression-based approaches, which have been proven to achieve excellent results. However, the ground-truth labels are usually inherently ambiguous in challenging cases such as motion blur, occlusions, and truncation, leading to poor performance measurement and lower levels of accuracy. In this paper, we propose Cofopose, which is a two-stage approach consisting of a person and keypoint detection transformers for 2D human p
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Wang, Xinran, Guoliang Li, and Feng Liu. "HRDS: A High-Dimensional Lightweight Keypoint Detection Network Enhancing HRNet with Dim-Channel and Space Gate Attention Using Kolmogorov-Arnold Networks." Electronics 14, no. 10 (2025): 2038. https://doi.org/10.3390/electronics14102038.

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Animal keypoint detection holds significant applications in fields such as biological behavior research and animal health monitoring. Although related research has reached a relatively mature stage of human keypoint detection, it still faces numerous challenges in the realm of animal keypoint detection. Firstly, there is a scarcity of keypoint detection datasets related to animals in public datasets. Secondly, existing solutions have adopted large-scale deep learning models to achieve higher accuracy, but these models are costly and difficult to widely promote within the industry. On the other
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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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Nguyen, Hung-Cuong, Thi-Hao Nguyen, Jakub Nowak, Aleksander Byrski, Agnieszka Siwocha, and Van-Hung Le. "Combined YOLOv5 and HRNet for High Accuracy 2D Keypoint and Human Pose Estimation." Journal of Artificial Intelligence and Soft Computing Research 12, no. 4 (2022): 281–98. http://dx.doi.org/10.2478/jaiscr-2022-0019.

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Abstract Two-dimensional human pose estimation has been widely applied in real-world applications such as sports analysis, medical fall detection, human-robot interaction, with many positive results obtained utilizing Convolutional Neural Networks (CNNs). Li et al. at CVPR 2020 proposed a study in which they achieved high accuracy in estimating 2D keypoints estimation/2D human pose estimation. However, the study performed estimation only on the cropped human image data. In this research, we propose a method for automatically detecting and estimating human poses in photos using a combination of
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Dissertations / Theses on the topic "Human keypoint detection"

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Runeskog, Henrik. "Continuous Balance Evaluation by Image Analysis of Live Video : Fall Prevention Through Pose Estimation." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297541.

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The deep learning technique Human Pose Estimation (or Human Keypoint Detection) is a promising field in tracking a person and identifying its posture. As posture and balance are two closely related concepts, the use of human pose estimation could be applied to fall prevention. By deriving the location of a persons Center of Mass and thereafter its Center of Pressure, one can evaluate the balance of a person without the use of force plates or sensors and solely using cameras. In this study, a human pose estimation model together with a predefined human weight distribution model were used to ext
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Book chapters on the topic "Human keypoint detection"

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Zhu, Zhilei, Wanli Dong, Xiaoming Gao, Anjie Peng, and Yuqin Luo. "Towards Human Keypoint Detection in Infrared Images." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1642-9_45.

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Chen, Zhikang, and Wei Qi Yan. "Real-Time Pose Recognition for Billiard Players Using Deep Learning." In Deep Learning, Reinforcement Learning, and the Rise of Intelligent Systems. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1738-9.ch010.

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In this book chapter, the authors propose a method for player pose recognition in billiards matches by combining keypoint extraction and an optimized transformer. Given that those human pose analysis methods usually require high labour costs, the authors explore deep learning methods to achieve real-time, high-precision pose recognition. Firstly, they utilize human key point detection technology to extract the key points of players from real-time videos and generate key points. Then, the key point data is input into the transformer model for pose analysis and recognition. In addition, the auth
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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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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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Conference papers on the topic "Human keypoint detection"

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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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Docekal, Jan, Jakub Rozlivek, Jiri Matas, and Matej Hoffmann. "Human Keypoint Detection for Close Proximity Human-Robot Interaction." In 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids). IEEE, 2022. http://dx.doi.org/10.1109/humanoids53995.2022.10000133.

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Song, Luona, Xin Guo, and Yiqi Fan. "Action Recognition in Video Using Human Keypoint Detection." In 2020 15th International Conference on Computer Science & Education (ICCSE). IEEE, 2020. http://dx.doi.org/10.1109/iccse49874.2020.9201857.

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Truong, Thinh Nguyen, Nham Nguyen Xuan, Trung Nguyen Quoc, and Vinh Truong Hoang. "Pushup Counting and Evaluating Based on Human Keypoint Detection." In 2022 9th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, 2022. http://dx.doi.org/10.1109/nics56915.2022.10013431.

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Zhou, Li, WenJie Li, Yu Chen, HanLing Liu, MentTing Yang, and ZiLong Liu. "Human Keypoint Change Detection for Video Violence Detection Based on Cascade Transformer." In 2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2023. http://dx.doi.org/10.1109/prai59366.2023.10331950.

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Zheng, Yi, Ruifeng Xiao, and Qiang He. "Human Keypoint-Guided Fall Detection: An Attention-Integrated GRU Approach." In ICMLCA 2023: 2023 4th International Conference on Machine Learning and Computer Application. ACM, 2023. http://dx.doi.org/10.1145/3650215.3650254.

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Ito, Yoshiki. "HOKEM: Human and Object Keypoint-Based Extension Module for Human-Object Interaction Detection." In 2023 IEEE International Conference on Image Processing (ICIP). IEEE, 2023. http://dx.doi.org/10.1109/icip49359.2023.10222203.

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Ko, Sang-Ki, Jae Gi Son, and Hyedong Jung. "Sign language recognition with recurrent neural network using human keypoint detection." In RACS '18: International Conference on Research in Adaptive and Convergent Systems. ACM, 2018. http://dx.doi.org/10.1145/3264746.3264805.

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Luo, Youtao, and Xiaoming Gao. "A lightweight network for human keypoint detection based on hybrid attention." In 2024 4th International Conference on Neural Networks, Information and Communication (NNICE). IEEE, 2024. http://dx.doi.org/10.1109/nnice61279.2024.10498687.

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Qi, Zezheng, and Yunjiang Lou. "3D Keypoint Detection of Lying Human Body Using an RGB-D Camera." In 2023 42nd Chinese Control Conference (CCC). IEEE, 2023. http://dx.doi.org/10.23919/ccc58697.2023.10240563.

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