Research on the Application of Multimedia Simulation Technology

Abstract:

The multimedia technology has been widely applied to many engineering fields. However, because the data contained in video content is very large, it is always being a difficult problem of computer data analysis and processing to analyze the video. Based on the content analysis, this paper takes use of many technologies aimed at the problem of video, such as analysis and processing of multimedia, simulation classification of computer and computer vision and so on. At the same time, combined with the model of color information semantics and the real target tracking principle, this paper builds model and designs the algorithm for the video simulation. At last, this paper makes trajectory extraction and recognition for the real process goals of football, establishing the simulation process of football. Through the numerical simulation, it is found that frames extracted from the video capture are different from each other in the process of real football game and the recognition rate and accuracy of simulation trajectory are also not the same. Among them, when frame is 85, the effects of recognition rate and accuracy are best, which respectively reach 80% and 89%. Thus, it gains a better simulation effect.

• Periodical: Advanced Materials Research (Volumes 846-847)
• Pages: 1780-1783
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.846-847.1780
• Online since: November 2013
• Authors: Meng Liu*, Sheng Dong Yang, Yang Wang
• Keywords: Color Semantic Identification, Computer Data Analysis, Football, Frames, Multimedia Simulation Technology, Target Tracking

References

[1] Yun Jian, Zhang Xu, Wei Xiaowu, etc. The strategies of cutting, controlling, dribbling, passing ball and movement in the RoboCup. [J]. Journal of Inner Mongolia university of science and technology, 2011, 28 (2) : 137-142.

[2] Gao Yong, Cai Xiaokai, Zeng Qingjun. Robocup simulation system research and program design for robot football [J]. Science, technology and engineering, 2010, 10 (27): 173-176.

[3] Li Jiyao, Chen Wei, Zhang Qi. Application of intelligent algorithm in robot football control simulation [J]. Automation technology and application. 2011, 26 (9) : 7-9.

[4] Liu Hongzhi. An improved shooting algorithm [J]. Journal of Harbin institute of technology, 2011, 36 (7) : 975 -977.

[5] Niu Zhenxing, Li Jie, Gao Xinbo. The semantic shot segmentation of soccer video based on color features and SVM [J]. Xd's journal, 2010, 37(4): 613-618.

[6] Shi Weiqiang. Design for robot soccer decision system based on reinforcement learning [D]. Master degree theses of master of central south university. 2011: 2-10.

[7] Liu Zhong, Li Haihong, Liu Quan. Reinforcement learning algorithms [J]. Computer engineering and design, 2012, 29 (22): 5805-5809.

[8] Xu Guotian. Research and improvement of the man-machine game algorithm [J]. Journal of Liaoning university, 2011, 33 (4): 368-370.

[9] Xiao Zheng, Zhang Shiyong. Multiple Agent conflict game reinforcement learning model based on regret value [J]. Journal of software engineering, 2011(3): 112-113.