A fog based ball tracking (FB2T) system using intelligent ball bees

Soccer is the most popular group game; it has a large number of fans all over the world. Although each complete soccer match lasts around one hour and a half, only have very few scenes attracting audiences been perfectly filmed. This filming has usually been done using static (fixed) cameras, and they cannot provide the same level of accuracy or entertainment given by mobile devices. Taking into our consideration that there are no schemes using mobile devices have been proposed to solve this problem till now. As periodic communication generates a huge amount of data; typical storage, computation, and communication resources are required. Hence, this paper aims at introducing a Fog computing mechanism that takes into consideration the requitrements of special mobility, low latency and location awareness. Our solution based on controlling the movement of mobile cameras mounted on a fleet of mobile bees. A bee is an autonomous camera-drone, which gives the audience the feeling of being a part of such sports competition. It is a special type of Unmanned Aerial Vehicle (UAV) equipped with a built-in processing unit, memory, high-speed camera and transceiver. The bee has the ability to film the movement of the ball, which has to be followed and filmed via object tracking principle. In fact, detecting and tracking the ball from the broadcast soccer video constitute a major challenge. In soccer matches, the ball moves most of the time and it is frequently occluded while its size and shape appearance vary over the time and between cameras. Moreover, the feature-based tracking methods are used to judge whether or not a sole object is the target as the features of the ball might be changed fast over frames and then we cannot manage to distinguish the ball from other objects by using these methods. Thus, the current study demonstrates an innovative technique for tracking a soccer ball from mobile cameras fixed on multiple ball bees.

[1]  T. Ohmi,et al.  An Accurate Eye Detection Method Using Elliptical Separability Filter and Combined Features , 2009 .

[2]  TaeYong Kim,et al.  Soccer Ball Tracking Using Dynamic Kalman Filter with Velocity Control , 2009, 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization.

[3]  Mihai Datcu,et al.  Information processing for unmanned aerial vehicles (UAVs) in surveying, mapping, and navigation , 2018, Geo spatial Inf. Sci..

[4]  Amany M. Sarhan,et al.  Resolving the ambiguity of real-time multiple object tracking using static cameras , 2011, Int. J. Intell. Syst. Technol. Appl..

[5]  Josef Halbinger,et al.  Video-Based Soccer Ball Detection in Difficult Situations , 2013, icSPORTS 2013.

[6]  Andreas Zell,et al.  Real-time object tracking for soccer-robots without color information , 2004, Robotics Auton. Syst..

[7]  Chuah Chai Wen,et al.  Fog Computing, Applications, Security and Challenges, Review , 2018, International Journal of Engineering & Technology.

[8]  Markus Niederoest,et al.  Automatic 3D reconstruction and visualization of microscopic objects from a monoscopic multifocus image sequence , 2003 .

[9]  Avinash G. Keskar,et al.  A deep learning ball tracking system in soccer videos , 2019, Opto-Electronics Review.

[10]  Danielle Catherine MacDonald Performance analysis of fielding and wicket-keeping in cricket to inform strength and conditioning practice , 2015 .

[11]  Stefano Secci,et al.  Cloud-based computation offloading for mobile devices: State of the art, challenges and opportunities , 2013, 2013 Future Network & Mobile Summit.

[12]  Yihong Gong,et al.  Feature design in soccer video indexing , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[13]  Alberto Del Bimbo,et al.  Soccer highlights detection and recognition using HMMs , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[14]  Steven L. Waslander,et al.  The Stanford testbed of autonomous rotorcraft for multi agent control (STARMAC) , 2004, The 23rd Digital Avionics Systems Conference (IEEE Cat. No.04CH37576).

[15]  Matías Alvarado,et al.  Analysis of Strategies in American Football Using Nash Equilibrium , 2014, AIMSA.

[16]  René van de Molengraft,et al.  From Vision to Realtime Motion Control for the RoboCup Domain , 2007, 2007 IEEE International Conference on Control Applications.

[17]  Qi Tian,et al.  A ball tracking framework for broadcast soccer video , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[18]  Hanqing Lu,et al.  An effective and fast soccer ball detection and tracking method , 2004, ICPR 2004.

[19]  Robert J. Woodham,et al.  Video analysis of hockey play in selected game situations , 2009, Image Vis. Comput..

[20]  Cataldo Guaragnella,et al.  A new algorithm for ball recognition using circle Hough transform and neural classifier , 2004, Pattern Recognit..

[21]  Jaime Sampaio,et al.  Statistical analyses of basketball team performance: understanding teams’ wins and losses according to a different index of ball possessions , 2003 .

[22]  Bernt Schiele,et al.  Video Segmentation with Superpixels , 2012, ACCV.

[23]  Avinash G. Keskar,et al.  A convolutional neural network based 3D ball tracking by detection in soccer videos , 2019, International Conference on Machine Vision.

[24]  Sateesh Addepalli,et al.  Fog computing and its role in the internet of things , 2012, MCC '12.

[25]  N. Owens,et al.  Hawk-eye tennis system , 2003 .

[26]  Tianjiang Wang,et al.  A long time tracking with BIN-NST and DRN , 2020, J. Ambient Intell. Humaniz. Comput..

[27]  Noel E. O'Connor,et al.  A virtual coaching environment for improving golf swing technique , 2010, SMVC '10.

[28]  Benyamin Kusumoputro,et al.  Attitude and altitude control of a quadcopter using neural network based direct inverse control scheme , 2017 .

[29]  James A. Sherwood,et al.  An Experimental Investigation of the Effect of Use on the Performance of Composite Baseball Bats (P274) , 2008 .

[30]  Wen Gao,et al.  A Scheme for Ball Detection and Tracking in Broadcast Soccer Video , 2005, PCM.

[31]  Shamik Sural,et al.  Ball detection from broadcast soccer videos using static and dynamic features , 2008, J. Vis. Commun. Image Represent..

[32]  Deping Chen Fuzzy obstacle avoidance optimization of soccer robot based on an improved genetic algorithm , 2020, J. Ambient Intell. Humaniz. Comput..

[33]  Ming Xu,et al.  Tracking the soccer ball using multiple fixed cameras , 2009, Comput. Vis. Image Underst..

[34]  Puteh Saad,et al.  Object Detection using Circular Hough Transform , 2005 .

[35]  Ezzeddine Zagrouba,et al.  Skeleton-based comparison of throwing motion for handball players , 2019, Journal of Ambient Intelligence and Humanized Computing.

[36]  Abhishek Kundu,et al.  Trajectory based soccer ball detection and tracking , 2015 .

[37]  Bulent Bayram,et al.  DETERMINING THE TECHNICAL STANDARDS OF PING PONG TABLE BY USING CLOSE RANGE PHOTOGRAMMETRY , 2012 .

[38]  Qi Tian,et al.  Trajectory-Based Ball Detection and Tracking in Broadcast Soccer Video , 2006, IEEE Transactions on Multimedia.

[39]  Youlian Zhu,et al.  An Improved Median Filtering Algorithm for Image Noise Reduction , 2012 .

[40]  A. Murat Tekalp,et al.  Automatic soccer video analysis and summarization , 2003, IEEE Trans. Image Process..

[41]  Teng Long Research on application of athlete gesture tracking algorithms based on deep learning , 2020, J. Ambient Intell. Humaniz. Comput..