A Survey of Video Based Action Recognition in Sports

Sport performance analysis which is crucial in sport practice is used to improve the performance of athletes during the games. Many studies and investigation have been done in detecting different movements of player for notational analysis using either sensor based or video based modality. Recently, vision based modality has become the research interest due to the vast development of video transmission online. There are tremendous experimental studies have been done using vision based modality in sport but only a few review study has been done previously. Hence, we provide a review study on the video based technique to recognize sport action toward establishing the automated notational analysis system. The paper will be organized into four parts. Firstly, we provide an overview of the current existing technologies of the video based sports intelligence systems. Secondly, we review the framework of action recognition in all fields before we further discuss the implementation of deep learning in vision based modality for sport actions. Finally, the paper summarizes the further trend and research direction in action recognition for sports using video approach. We believed that this review study would be very beneficial in providing a complete overview on video based action recognition in sports.

[1]  Peilong Xu Study on Moving Objects by Video Monitoring System of Recognition and Tracing Scheme , 2013 .

[2]  Guangchun Cheng,et al.  Advances in Human Action Recognition: A Survey , 2015, ArXiv.

[3]  Wen Gao,et al.  Event Tactic Analysis Based on Broadcast Sports Video , 2009, IEEE Trans. Multim..

[4]  Sridha Sridharan,et al.  Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[5]  Noel E. O'Connor,et al.  Real-time event classification in field sport videos , 2015, Signal Process. Image Commun..

[6]  Dima Damen,et al.  Recognizing linked events: Searching the space of feasible explanations , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Jian Zhang,et al.  Recognizing human actions from low-resolution videos by region-based mixture models , 2016, 2016 IEEE International Conference on Multimedia and Expo (ICME).

[8]  James J. Little,et al.  Classification of Puck Possession Events in Ice Hockey , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[9]  Yongdong Zhang,et al.  Automatic Detection and Analysis of Player Action in Moving Background Sports Video Sequences , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Cordelia Schmid,et al.  Action recognition by dense trajectories , 2011, CVPR 2011.

[11]  Xinbo Gao,et al.  Tactic analysis based on real-world ball trajectory in soccer video , 2012, Pattern Recognit..

[12]  Mubarak Shah,et al.  Learning a Deep Model for Human Action Recognition from Novel Viewpoints , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Lin Li,et al.  Moving Vehicle Recognition and Feature Extraction From Tunnel Monitoring Videos , 2013 .

[14]  Guizhong Liu,et al.  Field lines and players detection and recognition in soccer video , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[15]  Kazimierz Choros Detection of Tennis Court Lines for Sport Video Categorization , 2012, ICCCI.

[16]  Chieh-Li Chen,et al.  Tennis Video 2.0: A new presentation of sports videos with content separation and rendering , 2011, J. Vis. Commun. Image Represent..

[17]  Limin Wang,et al.  Action recognition with trajectory-pooled deep-convolutional descriptors , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  David Herman,et al.  Human gesture recognition using top view depth data obtained from Kinect sensor , 2015 .

[19]  Mike D Hughes,et al.  The use of performance indicators in performance analysis , 2002, Journal of sports sciences.

[20]  Noel E. O'Connor,et al.  TennisSense: A platform for extracting semantic information from multi-camera tennis data , 2009, 2009 16th International Conference on Digital Signal Processing.

[21]  Ling Shao,et al.  Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier , 2017, IEEE Transactions on Image Processing.

[22]  Ronald Poppe,et al.  A survey on vision-based human action recognition , 2010, Image Vis. Comput..

[23]  Tatsuya Harada,et al.  Football Action Recognition Using Hierarchical LSTM , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[24]  Amir Roshan Zamir,et al.  Action Recognition in Realistic Sports Videos , 2014 .

[25]  A. Lees Technique analysis in sports: a critical review , 2002, Journal of sports sciences.

[26]  Guanglong Du,et al.  Human-Manipulator Interface Using Particle Filter , 2014, TheScientificWorldJournal.

[27]  Jürgen Schmidhuber,et al.  Learning Precise Timing with LSTM Recurrent Networks , 2003, J. Mach. Learn. Res..

[28]  Adil Mehmood Khan,et al.  Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks , 2017, 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[29]  Chunheng Wang,et al.  Action recognition via structured codebook construction , 2014, Signal Process. Image Commun..

[30]  Hong Zhang,et al.  Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation , 2013, Sensors.

[31]  Brian Caulfield,et al.  Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation , 2017, JMIR mHealth and uHealth.

[32]  Christian Wolf,et al.  Action Classification in Soccer Videos with Long Short-Term Memory Recurrent Neural Networks , 2010, ICANN.

[33]  Haifeng Lin,et al.  Boost Action Recognition through Computed Volume , 2013 .

[34]  Naokazu Yokoya,et al.  Human action recognition-based video summarization for RGB-D personal sports video , 2016, 2016 IEEE International Conference on Multimedia and Expo (ICME).

[35]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[36]  Samina Khalid,et al.  A survey of feature selection and feature extraction techniques in machine learning , 2014, 2014 Science and Information Conference.

[37]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[38]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[39]  F. Lautenbach A laboratory study on attentional bias as an underlying mechanism affecting the link between cortisol and performance, leading to a discussion on the nature of the stressor (artificial vs. psychosocial) , 2017, Physiology & Behavior.

[40]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.