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

Ali Sohani, . Rafi Ullah, Athaul Rai, Owais Karni

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.


Keywords


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

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References


Pires, Ivan Miguel, Nuno M. Garcia, and Maria Cristina Canavarro Teixeira. "Calculation of Jump Flight Time using a Mobile Device." In HEALTHINF, pp. 293-303. 2015.

Moir, Gavin L. "Three different methods of calculating vertical jump height from force platform data in men and women." Measurement in Physical Education and Exercise Science 12, no. 4 (2008): 207-218.

Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 1, pp. 886-893. IEEE, 2005.

Tsai, Grace. "Histogram of oriented gradients." University of Michigan 1, no. 1 (2010): 1-17.

Qiu, Shaohua, Gongjian Wen, Zhipeng Deng, Jia Liu, and Yaxiang Fan. "Accurate non-maximum suppression for object detection in high-resolution remote sensing images." Remote Sensing Letters 9, no. 3 (2018): 238-247.

Fan, Y., G. Wen, and S. Qiu. "An Accurate Framework for Arbitrary View Pedestrian Detection in Images." In Journal of Physics: Conference Series, vol. 960, no. 1, p. 012022. IOP Publishing, 2018.

Zhao, Chen, Zhao Yuqing, Yang Luqiang, Zhou Qiao, Gao Yanyu, Liu Cunrui, Wang Zihui, and Shi Ling. "Image Processing Techniques Based on OpenCV Spray Equipment." Journal of Agricultural Mechanization Research 6 (2018): 037.

George, Rana Praful, and V. Prakash. "Real-Time Human Detection and Tracking Using Quadcopter." In Intelligent Embedded Systems, pp. 301-312. Springer, Singapore, 2018.

Zhang, Shanshan, Rodrigo Benenson, Mohamed Omran, Jan Hosang, and Bernt Schiele. "Towards reaching human performance in pedestrian detection." IEEE transactions on pattern analysis and machine intelligence 40, no. 4 (2018): 973-986.

Li, Qingliang, Lili Xu, Pengliang Zheng, and Fei He. "A Local Neighborhood Constraint Method for SIFT Features Matching." In Recent Developments in Data Science and Business Analytics, pp. 313-320. Springer, Cham, 2018.

Tareen, Shaharyar Ahmed Khan, and Zahra Saleem. "A Comparative Analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK." (2018).

He, Tao, Yong Wei, Zhijun Liu, Guorong Qing, and Defen Zhang. "Content based image retrieval method based on SIFT feature." In Intelligent Transportation, Big Data & Smart City (ICITBS), 2018 International Conference on, pp. 649-652. IEEE, 2018.

http://www.topendsports.com/testing/equipment-verticaljump.htm

https://www.plux.info/index.php/en/

Linthorne, Nicholas P., Analysis of standing vertical jumps using a force platform. Am. J. Phys., Vol. 69, No. 11, November 2001.

https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_setup/py_intro/py_intro.html

http://scikit-image.org/ last visited 6-Oct-2017

http://pyimagesearch.com last visited 9-Oct-2017

http://redlineathletics.com/

hhttps://www.whatsmyvertical.com/

http://www.kinovea.org/en/

https://www.siliconcoach.com/

http://www.sportsmotion.com/

https://www.peakperform.com/

http://www.sportsclipmaker.com/


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