Kinematic parameter estimation using close range photogrammetry for sport applications

Luz Alejandra Magre Colorado; Juan Carlos Martínez Santos

doi:10.1117/12.2208354

22 December 2015 Kinematic parameter estimation using close range photogrammetry for sport applications

Luz Alejandra Magre Colorado, Juan Carlos Martínez Santos

Proceedings Volume 9681, 11th International Symposium on Medical Information Processing and Analysis; 96810M (2015) https://doi.org/10.1117/12.2208354
Event: 11th International Symposium on Medical Information Processing and Analysis (SIPAIM 2015), 2015, Cuenca, Ecuador

Abstract

In this article, we show the development of a low-cost hardware/software system based on close range photogrammetry to track the movement of a person performing weightlifting. The goal is to reduce the costs to the trainers and athletes dedicated to this sport when it comes to analyze the performance of the sportsman and avoid injuries or accidents. We used a web-cam as the data acquisition hardware and develop the software stack in Processing using the OpenCV library. Our algorithm extracts size, position, velocity, and acceleration measurements of the bar along the course of the exercise. We present detailed characteristics of the system with their results in a controlled setting. The current work improves the detection and tracking capabilities from a previous version of this system by using HSV color model instead of RGB. Preliminary results show that the system is able to profile the movement of the bar as well as determine the size, position, velocity, and acceleration values of a marker/target in scene. The average error finding the size of object at four meters of distance is less than 4%, and the error of the acceleration value is 1.01% in average.

Citation Download Citation

Luz Alejandra Magre Colorado and Juan Carlos Martínez Santos "Kinematic parameter estimation using close range photogrammetry for sport applications", Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 96810M (22 December 2015); https://doi.org/10.1117/12.2208354

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