Method for Extracting Data During Flight Phase of Ski Jumping Based on Monocular Video

doi:10.16182/j.issn1004731x.joss.22-1408

Abstract

Abstract:

To solve the problem of the high difficulty factor of ski jumping and the difficulty of extracting data of this sport due to the danger of invasive devices such as wearable sensors and high price, a method for extracting data during the flight phase of ski jumping based on monocular video is proposed. The distortion and background clutter of the monocular video are preprocessed.The distortion of the captured images is corrected by calibrating camera parameters, and the background is removed by the inter-frame difference method. The human pose recognition library, namely OpenPose is used to initially identify the joint position of the athlete and obtain the 2Dpixel coordinates of each joint point in each frame. An iterative fitting algorithm is proposed to correct the joint points with errors in recognition by combining the pose characteristics of the athlete. The athletes' motion features are extracted and calculated according to the modified joint points, and the generated human models are applied to compare with the athletes' poses in the video. The experimental results show that the iterative fitting algorithm improves the accuracy and precision of joint point recognition, and the SMPL(skinned multi-person linear) 3D human body model generated after joint point correction is more suitable for reality, which proves the effectiveness of the algorithm for joint point correction.

Key words: flight phase of ski jumping, human joint point detection, coordinate correction, 2D track extraction, comparison of 3D human body model

CLC Number:

TP391.9

Shen Ziyi, Yang Meng, Yang Chao, Tang Weidi, Wu Xie, Liu Yu, Sheng Bin. Method for Extracting Data During Flight Phase of Ski Jumping Based on Monocular Video[J]. Journal of System Simulation, 2023, 35(9): 2035-2044.

Figures/Tables 16

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Table 1

Joint angles

关节角	角度
$α 0$	39.73
$α 1$	53.62
$α 2$	40.58
$α 3$	22.76
$α 4$	30.86

Table 1

Fig. 11

Fig. 12

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Table 2

Fig. 14

References 20

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算法	PCKh	mAP
OpenPose	63.24	67.91
本文修正后	78.56	85.08
Lite-HRNet	66.13	70.27
本文修正后	79.52	83.26

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