基于Posenet和k-NN的瑜伽姿势检测

Diwakar Shah, Vidya Rautela, Chirag Sharma, Angelin Florence A
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引用次数: 3

摘要

瑜伽提供了多种体式,身体部位之间的角度在这里起着重要的作用。这个项目是一个非营利性的系统,致力于用类似瑜伽的姿势来锻炼核心肌肉。在练习虚拟瑜伽体式时,所提出的技术可以完美地检测人体的位置。为了解决形成的角度与原始值的分歧,采用了余弦相似技术。由于关键角度是由关键角度的组合组成的,因此必须解决多个维度。该系统检测实际位置和目标位置之间的差异,并通过提供实时图像输出和必要的指令来纠正用户识别的姿势。本研究利用计算机视觉技术和Open pose(开源库)来估计个体的瑜伽姿势。在大多数情况下,所建议的方法在实现实时速度的同时保持了较高的精度。该模型用90%的数据进行训练,用10%的数据进行实时测试,准确率达到94%。
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Yoga Pose Detection Using Posenet and k-NN
Yoga offers a wide range of asanas, and the angle between body parts plays an important role here. This project carries a non-profit system that strives to develop core muscles using yoga-like poses. While practicing yoga asanas virtually, the proposed technique perfectly detects the human position. To contemplate the dissension of the angle formed with original values, the cosine similarity technique is applied. Multiple dimensions must be addressed since crucial angles are made up of a critical combination of angles. This system detects the difference between the actual and target positions and corrects the user by delivering real-time image output and necessary instructions to correct the identified pose. Human poses estimation is utilized in this research to estimate an individual's Yoga position using computer vision techniques and Open pose (open-source library). In most circumstances, the suggested method retains high accuracy while achieving real-time speed. The proposed model was trained with 90% of data and tested with 10% of same with real-time testing, resulting 94 % of accuracy.
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