Karate First Kata Performance Analysis and Evaluation with Computer Vision and Machine Learning

Elkess, George; Elmoushy, Seif; Atia, Ayman

doi:10.1109/miucc58832.2023.10278313

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…MPP estimates 33 landmarks of the human body joints using the BlazePose model [21]. Research analyzing motions in activities of daily living [19,20] and karate using MPP has recently gained momentum [22]. MPP uses a detector-tracker ML pipeline.…”

Section: Skeleton Modelmentioning

confidence: 99%

Human Joint Angle Estimation Using Deep Learning-Based Three-Dimensional Human Pose Estimation for Application in a Real Environment

Choi,

Ha,

Son

et al. 2024

Sensors

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Human pose estimation (HPE) is a technique used in computer vision and artificial intelligence to detect and track human body parts and poses using images or videos. Widely used in augmented reality, animation, fitness applications, and surveillance, HPE methods that employ monocular cameras are highly versatile and applicable to standard videos and CCTV footage. These methods have evolved from two-dimensional (2D) to three-dimensional (3D) pose estimation. However, in real-world environments, current 3D HPE methods trained on laboratory-based motion capture data encounter challenges, such as limited training data, depth ambiguity, left/right switching, and issues with occlusions. In this study, four 3D HPE methods were compared based on their strengths and weaknesses using real-world videos. Joint position correction techniques were proposed to eliminate and correct anomalies such as left/right inversion and false detections of joint positions in daily life motions. Joint angle trajectories were obtained for intuitive and informative human activity recognition using an optimization method based on a 3D humanoid simulator, with the joint position corrected by the proposed technique as the input. The efficacy of the proposed method was verified by applying it to three types of freehand gymnastic exercises and comparing the joint angle trajectories during motion.

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Section: Skeleton Modelmentioning

confidence: 99%

Human Joint Angle Estimation Using Deep Learning-Based Three-Dimensional Human Pose Estimation for Application in a Real Environment

Choi,

Ha,

Son

et al. 2024

Sensors

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show abstract

“…MPP estimates a total of 33 landmarks of human body joints based on the Blaze Pose Model. Recently, research on analyzing motions in Activities of Daily Living [8] and activities such as karate using MPP has been gaining momentum [9]. HybrIK is an inverse kinematics solution that combines the strengths of 3D keypoint estimation and body mesh recovery into a unified framework.…”

Section: Introductionmentioning

confidence: 99%

Comparative and Improvement Study of 3D Human Pose Estimation Algorithms using Monocular Cameras

Choi,

Ha,

Kim

2024

Preprint

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Human Pose Estimation (HPE) is a technique in computer vision and AI for detecting and tracking human body parts and poses from images or videos. Widely used in augmented reality, animation, fitness applications, and surveillance, HPE methods using monocular cameras are highly versatile due to their applicability in standard video and CCTV footage. These methods have evolved from 2D to 3D pose estimation. However, current 3D HPE methods trained on laboratory-based motion capture data encounter challenges such as limited training data, depth perception ambiguity, left/right switching, and issues with occlusions when applied in real-world environments. This study compares two representative 3D HPE methods by assessing their strengths and weaknesses with real-world videos. Then, we propose data processing techniques to eliminate and correct anomalies like left/right inversion and false detections of joint positions in daily life motions. Finally, we obtain joint angle trajectories using an optimization method based on a 3D humanoid simulator, taking as input the joint coordinate data corrected by applying the proposed human joint data processing technique. The efficacy of the proposed 3D HPE method is verified by applying it to three-dimensional freehand gymnastics exercises and comparing the joint angle trajectories during the motion.

show abstract

Karate First Kata Performance Analysis and Evaluation with Computer Vision and Machine Learning

Cited by 2 publications

References 16 publications

Human Joint Angle Estimation Using Deep Learning-Based Three-Dimensional Human Pose Estimation for Application in a Real Environment

Human Joint Angle Estimation Using Deep Learning-Based Three-Dimensional Human Pose Estimation for Application in a Real Environment

Comparative and Improvement Study of 3D Human Pose Estimation Algorithms using Monocular Cameras

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