Detection of launch frame in long jump videos using computer vision and discreet computation

Pose estimation, a computer vision technique, can be used to develop a quantitative feedback training tool for long jumping. Key performance indicators (KPIs) such as launch velocity would allow a long jumping athlete to optimize their technique while training. However, these KPIs need a prior knowledge of when the athlete jumped, referred to as the launch frame in the context of videos and computer vision. Thus, an algorithm for estimating the launch frame was made using the OpenPose Demo and Matlab. The algorithm estimates the launch frame to within 0.8±0.91 frames. Implementing the algorithm into a training tool would give an athlete real-time, quantitative feedback from a video. This process of developing an algorithm to flag an event can be used in other sports as well, especially with the rise of KPIs in the sports industry (e.g. launch angle and velocity in baseball).

Name thesis:degree_name

Bachelor

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Muñiz, Pablo E.(Muñiz Aponte)

Advisor dc:contributor.advisor

• Anette Hosoi.

• Mechanical Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123277

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123277