Research on sports video detection technology motion 3D reconstruction based on hidden Markov model

The difficulty of sports video detection technology lies in how to detect the end point segment from the complex video speech environment, and the artificial intelligence technology is still in the research stage. Based on this, this study builds a model based on the hidden Markov model. At the same time, the video file noise reduction processing is performed by the spectral subtraction noise reduction algorithm of the complex domain extension. Moreover, combined with the actual situation of sports competitions, this paper proposes an endpoint detection algorithm based on variance characteristics, and comprehensively designs a speech recognition model based on Markov model. In order to verify the validity of the model, the performance of the model is verified by an example, and the real sports competition is taken as the research object, and the accuracy rate and the recall rate are set as performance indicators. The research shows that the model proposed in this study performs well in both accuracy and performance rate and can be used as a reference for artificial intelligence application to sports video detection technology.

[1]  Huang-Chia Shih,et al.  A Survey of Content-Aware Video Analysis for Sports , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Ali Javed,et al.  An Efficient Framework for Automatic Highlights Generation from Sports Videos , 2016, IEEE Signal Processing Letters.

[3]  Jeffrey Mark Siskind,et al.  Action Recognition by Time Series of Retinotopic Appearance and Motion Features , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Klamer Schutte,et al.  Selection of negative samples and two-stage combination of multiple features for action detection in thousands of videos , 2013, Machine Vision and Applications.

[5]  Takashi Matsumoto,et al.  Predicting viewer-perceived activity/dominance in soccer games with stick-breaking HMM using data from a fixed set of cameras , 2014, Multimedia Tools and Applications.

[6]  David Windridge,et al.  A Novel Markov Logic Rule Induction Strategy for Characterizing Sports Video Footage , 2015, IEEE MultiMedia.

[7]  Jerry Lin,et al.  Beyond Gaming: The Utility of Video Games for Sports Performance , 2014, Int. J. Gaming Comput. Mediat. Simulations.

[8]  P Silva,et al.  Assessing physical activity intensity by video analysis. , 2015, Physiological measurement.

[9]  H. William Gageler,et al.  Automatic jump detection method for athlete monitoring and performance in volleyball , 2015 .

[10]  Wei Liu,et al.  Unsupervised mining of visually consistent shots for sports genre categorization over large-scale database , 2015, Telecommun. Syst..

[11]  Barry S Mason,et al.  Validity and reliability of an inertial sensor for wheelchair court sports performance. , 2014, Journal of applied biomechanics.

[12]  Chien-Li Chou,et al.  2D Histogram-based player localization in broadcast volleyball videos , 2015, Multimedia Systems.

[13]  Baoxin Li,et al.  Semantic Cues Enhanced Multimodality Multistream CNN for Action Recognition , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Ling-Hwei Chen,et al.  A novel method for slow motion replay detection in broadcast basketball video , 2015, Multimedia Tools and Applications.

[15]  Bhabatosh Chanda,et al.  A shot detection technique using linear regression of shot transition pattern , 2014, Multimedia Tools and Applications.

[16]  Zhengang Wei,et al.  Automatic analysis of complex athlete techniques in broadcast taekwondo video , 2017, Multimedia Tools and Applications.

[17]  Uday Pandit Khot,et al.  Smart Sensing Using Bayesian Network for Computer Aided Diagnostic Systems , 2015 .

[18]  Nasser Kehtarnavaz,et al.  Ice-hockey puck detection and tracking for video highlighting , 2016, Signal Image Video Process..

[19]  Ling-Hwei Chen,et al.  A novel approach for semantic event extraction from sports webcast text , 2012, Multimedia Tools and Applications.

[20]  Kewei Tu,et al.  Joint Video and Text Parsing for Understanding Events and Answering Queries , 2013, IEEE MultiMedia.

[21]  Luis Torres,et al.  Automatic summarization of soccer highlights using audio-visual descriptors , 2015, SpringerPlus.

[22]  Wen-Jiin Tsai,et al.  Incorporating frequent pattern analysis into multimodal HMM event classification for baseball videos , 2015, Multimedia Tools and Applications.

[23]  B. Heiderscheit,et al.  Reliability of a Qualitative Video Analysis for Running. , 2016, The Journal of orthopaedic and sports physical therapy.

[24]  Sadie Creese,et al.  Identifying attack patterns for insider threat detection , 2015 .

[25]  D. Kokona,et al.  Effects of novel synthetic microneurotrophins in diabetic retinopathy , 2015, SpringerPlus.

[26]  Sung Yong Shin,et al.  Creating Walk-Through Images from a Video Sequence of a Dynamic Scene , 2004, Presence: Teleoperators & Virtual Environments.