Abstract
In this paper, we propose an adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis and prediction through the study and analysis of sports videos. The features with more discriminative power are selected from the set of positive and negative templates using a feature selection mechanism, and a sparse discriminative model is constructed by combining a confidence value metric strategy. The sparse generative model is constructed by combining L1 regularization and subspace representation, which retains sufficient representational power while dealing with outliers. To overcome the shortcomings of the traditional multiplicative fusion mechanism, this paper proposes an adaptive selection mechanism based on Euclidean distance, which aims to detect deteriorating models in time during the dynamic tracking process and adopt corresponding strategies to construct more reasonable likelihood functions. Based on the Bayesian citation framework, the adaptive selection mechanism is used to combine the sparse discriminative model and the sparse generative model. Also, different online updating strategies are adopted for the template set and Principal Component Analysis (PCA) subspace to alleviate the drift problem while ensuring that the algorithm can adapt to the changes of target appearance in the dynamic tracking environment. Through quantitative and qualitative evaluation of the experimental results, it is verified that the algorithm proposed in this paper has stronger robustness compared with other classical algorithms. Our proposed visual object tracking algorithm not only outperforms existing visual object tracking algorithms in terms of accuracy, success rate, accuracy, and robustness but also achieve the performance required for real-time tracking in terms of execution speed on the central processing unit (CPU). This paper provides an in-depth analysis and discussion of the adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis. Using a variety of county-level algorithms for analysis and multiple solutions to improve the accuracy of the results, we obtain a more efficient and accurate algorithm.
Highlights
Humans can quickly obtain a large amount of information from video images, and that visual information is the main way to recognize the environment
While the human is the main subject of activities in society, the research related to the human body in images becomes the focus, including human body detection, human body tracking, human body pose estimation, human behavior recognition, and prediction. is paper focuses on action recognition, where there is the interaction between the human body and the environment, and the human body’s pose and human motion are the basis
It is found that the proposed algorithm converges to a minimum after a finite number of iterations in all the experiments conducted in this paper. e algorithm is implemented in Matlab R2014a, and all experiments are performed on a computer configured with a 3.3 GHz Intel i5-4590 central processing unit (CPU) and 8 G RAM. e computational speed of the proposed algorithm is about 0.36 sec/ frame when extracting multimodal low-level features such as GJF, LDD, and LCD
Summary
Humans can quickly obtain a large amount of information from video images, and that visual information is the main way to recognize the environment. Human detection, tracking, and pose estimation can be used to analyze a human position, motion, and pose and to achieve action recognition. E research on this topic can be summarized in two important processes: one is human target tracking and localization, the other is action recognition and behavior understanding, the former is the foundation, and the latter is a higher level of application. Human action element extraction includes designing a threshold-free human detector using video foreground prior probability, online tracking of single and multiple targets, and human pose estimation using information correlation of video time and space dimensions [5]. Is detector integrates the foreground probability with the human model response of the image and learns the optimal decision parameters from the data so that better results can be obtained without manually adjusting the detection threshold. The foreground probability can be used to assist in generating candidate detection windows, and the number of windows to be detected in this method is less than that of traditional methods with the same recall rate
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