A Deep Learning and Clustering Extraction Mechanism for Recognizing the Actions of Athletes in Sports

Abstract

In sports, the essence of a complete technical action is a complete information structure pattern and the athlete’s judgment of the action is actually the identification of the movement information structure pattern. Action recognition refers to the ability of the human brain to distinguish a perceived action from other actions and obtain predictive response information when it identifies and confirms it according to the constantly changing motion information on the field. Action recognition mainly includes two aspects: one is to obtain the required action information based on visual observation and the other is to judge the action based on the obtained action information, but the neuropsychological mechanism of this process is still unknown. In this paper, a new key frame extraction method based on the clustering algorithm and multifeature fusion is proposed for sports videos with complex content, many scenes, and rich actions. First, a variety of features are fused, and then, similarity measurement can be used to describe videos with complex content more completely and comprehensively; second, a clustering algorithm is used to cluster sports video sequences according to scenes, eliminating the need for shots in the case of many scenes. It is difficult and complicated to detect segmentation; third, extracting key frames according to the minimum motion standard can more accurately represent the video content with rich actions. At the same time, the clustering algorithm used in this paper is improved to enhance the offline computing efficiency of the key frame extraction system. Based on the analysis of the advantages and disadvantages of the classical convolutional neural network and recurrent neural network algorithms in deep learning, this paper proposes an improved convolutional network and optimization based on the recognition and analysis of human actions under complex scenes, complex actions, and fast motion compared to post-neural network and hybrid neural network algorithm. Experiments show that the algorithm achieves similar human observation of athletes’ training execution and completion. Compared with other algorithms, it has been verified that it has very high learning rate and accuracy for the athlete’s action recognition.

Automatic Analysis and Event Detection Technology of Sports Competition Video Based on Deep Learning

“…Event detection technology refers to a set of tools and methods aimed at identifying and analyzing significant occurrences or patterns within a given data stream or system [1]. This technology often relies on advanced algorithms and machine learning techniques to sift through vast amounts of data in real-time, pinpointing events of interest and flagging them for further analysis or action [2]. One common application of event detection technology is in the realm of security and surveillance.…”

VIASCKDE Index: A Novel Internal Cluster Validity Index for Arbitrary-Shaped Clusters Based on the Kernel Density Estimation

“…Clustering approaches are unsupervised learning techniques that separate data into groups called clusters according to the similarities and dissimilarities among the data [ 1 , 2 ]. The DBSCAN [ 3 ], kmeans [ 4 ], BIRCH [ 5 ], Spectral Clustering [ 6 ], Agglomerative Clustering [ 7 ], HDBSCAN [ 8 ], Affinity Propagation [ 9 ], and OPTICS [ 10 ] are some examples of them, and they are used in many fields such as pattern recognition [ 11 – 13 ], machine learning [ 14 – 16 ], data mining [ 17 , 18 ], web mining [ 1 , 19 ], bioinformatics [ 20 , 21 ], and streaming data mining [ 22 , 23 ]. On the other hand, measuring the performance of any proposed clustering approach is also an important issue because each algorithm has its special point of view, and the results of each clustering technique vary.…”

Retracted: A Deep Learning and Clustering Extraction Mechanism for Recognizing the Actions of Athletes in Sports

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”