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The Event Detection of Serving a Ball in Sports Video
Jingmeng Sun,Yang Liu,Gang Liu1,Yang Wang,Haitao Yang 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.3
Tennis video’s serve events were detected. In other words, athletes area were obtained to calculate particle of athletes area and particle shift in adjacent frame by using background subtraction algorithm. And then tennis video’s serve events were inferred, according to specific constraints on serve, through establishing ontology and setting SWRL rules.
Research on Long Shot Segmentation in Basketball Video
ShengBo Liao,Jingmeng Sun,Haitao Yang 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.12
In the basketball segmentation in long shots, Gauss filter is adopted to smooth noise in the image firstly. Secondly, the background is separated by the difference of inter-frame and the connected regions are labeled. Thirdly, a strategy is designed to identify basketball by the characteristic of basketball. Finally, the edge deviation is revised and the optimal result is obtained using improved Snakes.
Research on Close Shot Segmentation in Sports Video
Yang Wang,Jingmeng Sun,Yifei Liu,Yueqiu Han 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.1
The basketball segmentation in close shots is studied. According to the consistency of basketball color, color segmentation is implemented. Then with the help of steerable filters, the method detects image edges and the orientations of them. Finally, based on edges and their orientations, Hough transform is improved to get the circle center and the radius of basketball.
Detection of Corner Event Based on Hidden Markov Model in Soccer Video
Haitao Yang,Jia Wang,Jingmeng Sun 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.12
A corner event detection method based on HMM (Hidden Markov Model) in soccer videos. Through analysis of the semantic structure of corner events, define and extract six multi- modal semantic clues to describe shot sequences, which constitute observation sequences as the HMM model input. By the iterative training of the HMM model and the continuous optimization of model parameters, construct the HMM model of corner events. From two aspects of audio and video, dig the inherent pattern of corner events and realize corner events automatic detection based on HMM accurately. The experimental results show that the present method achieves 89.6% recall and 96.3% precision, which has the better performance.
Research on Analysis of Sports Video based on the Statistics
Haitao Yang,Jia Wang,Jingmeng Sun 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.12
We turn to statistical-based methods and propose a statistical inference approach by using Dynamic Bayesian Network, which is able to learn automatically from data set. By soccer video analysis is as an example, the proposed method is verified by experiment. We extract the color, shape and other low-level features from the video, to detect and identify 5 kinds of high-level semantic events using dynamic Bayesian network model. The experimental results show that our method is effective.
Research on Analysis of Sports Video Multi-Pattern Fusion
Jia Wang,Haitao Yang,Yang Wang,Jingmeng Sun 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.6
In order to effectively integrate multimodal information and multilayer constraints, we present a unified probabilistic framework for sports video analysis. Based the framework, three instances of statistical models are constructed and compared. Experimental results indicate our method with multimodal fusion processes semantic events in sports video more effectively.
Research on Target Detection in Sports Video
Gang Liu,Yang Liu,Yang Wang,Jingmeng Sun 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.2
For moving objects detection, a background subtraction algorithm based on adaptive Gaussian mixture model is proposed in order to extract moving regions. The OTSU algorithm is researched in order to adapt to the changes in the background images; In order to accelerate model updating rate, a novel mechanism is the combination of expected sufficient statistics and L-recent window.