Performing an 'Athletic Movement Assessment' for Sports Jump Using State of the Art Video Analysis and Heuristics Techniques Like Body Detection and Displacement Assessment

Authors

  • Ali Sohani Data Science Department, Cubix.co
  • . Rafi Ullah Data Science Department, Cubix.co
  • Athaul Rai Data Science Department, Cubix.co
  • Owais Karni Data Science Department, Cubix.co

Keywords:

Vertical Jump Height, Sensor-less measurement, Video Analysis, Athletic Movement Assessment, Histogram of Oriented Gradient.

Abstract

This paper proposes a some novel and state of the art technique for analyzing the Athletic Movement (Vertical Jump) and feats  by analyzing video frame by frame.  Most common method to analyze "Athletic Movement" such as Jump and feats accomplished in them are either an observations made by an human expert / coach, or they are the values captured by measurement devices in the suit or wearables attached to the body of an athlete. Where former requires an access to the human expert, the later requires the special kind of a hardware / sensor that has capability to extract the body movement statistics with respect to time and space. Both methods are pretty accurate but due to their overhead in terms of necessity / dependence on 3rd party system or person. Not to mention along with the cost such methods come up with, they are often inaccessible in situations where one's just home practicing or when an athlete is just trying out something in own backyard or Gym (personal zones). Our target was here to reduce those dependencies and create such heuristics and algorithms that can help an individual athlete to assess the feats like Jump, Run, and Leap, without using any 3rd party systems, and be able to approximate the feats and compare them with the existing ones using only the cellphone device in their pocket. This paper focused on Jump sport. The system processed video frame by frame and Applying Histogram Of Oriented Gradient Technique to find Human in Frame and then track human from  initial to last and we are capable now to calculate pixel distance covered by human in Jump. We used some values like human height to find physical distance covered, Frame Per Frame (FPS) of video, Markers on screen of mobile while recording videos.

To validate the algorithm results, a number of experiments were performed and then compare with the actual vertical jump height and derive a statistical relation between the proposed methodology and the traditional techniques. Proposed technique can also be used for calculating different statistics of sport person.

Author Biographies

Ali Sohani, Data Science Department, Cubix.co

Chief Data Scientist and Chief Technical Officer at Cubix

. Rafi Ullah, Data Science Department, Cubix.co

Senior Data Scientist at Cubix

Athaul Rai, Data Science Department, Cubix.co

Junior Data Scientist at Cubix

Owais Karni, Data Science Department, Cubix.co

Junior Data Scientist at Cubix

References

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Published

2018-08-03

How to Cite

Sohani, A., Rafi Ullah, ., Rai, A., & Karni, O. (2018). Performing an ’Athletic Movement Assessment’ for Sports Jump Using State of the Art Video Analysis and Heuristics Techniques Like Body Detection and Displacement Assessment. American Scientific Research Journal for Engineering, Technology, and Sciences, 45(1), 171–184. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/4210

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Articles