• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.89-98
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• 2007

For browsing, searching, and manipulating video documents, an indexing technique to describe video contents is required. Until now, the indexing process is mostly carried out by specialists who manually assign a few keywords to the video contents and thereby this work becomes an expensive and time consuming task. Therefore, automatic classification of video content is necessary. We propose a fully automatic and computationally efficient method for analysis and summarization of spots news video for 5 spots news video such as soccer, golf, baseball, basketball and volleyball. First of all, spots news videos are classified as anchor-person Shots, and the other shots are classified as news reports shots. Shot classification is based on image preprocessing and color features of the anchor-person shots. We then use the dominant color of the field and motion features for analysis of sports shots, Finally, sports shots are classified into five genre type. We achieved an overall average classification accuracy of 75% on sports news videos with 241 scenes. Therefore, the proposed method can be further used to search news video for individual sports news and sports highlights.

• Korean Journal of Applied Biomechanics
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• v.18 no.2
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• pp.59-64
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• 2008

D. W. KANG, J. S. CHOI, and G. R. TACK, A Study on Real-Time Sports Activity Classification & Monitoring Using a Tri-axial Accelerometer. Korean Jouranl of Sport Biomechanics, Vol. 18, No. 2, pp. 59-64, 2008. This study was conducted to study the real-time sports activity classification and monitoring using single waist mounted tri-axial accelerometer. This monitoring system detects events of sports activities such as walking, running, cycling, transitions between movements, resting and emergency event of falls. Accelerometer module was developed small and easily attachable on waist using wireless communication system which does not constrain sports activities. The sensor signal was transferred to PC and each movement pattern was classified using the developed algorithm in real-time environment. To evaluate proposed algorithm, experiment was performed with several sports activities such as walking, running, cycling movement for 100sec each and falls, transition movements(sit to stand, lie to stand, stand to sit, lie to sit, stand to lie and sit to lie) for 20 times each with 5 healthy subjects. The results showed that successful detection rate of the system for all activities was 95.4%. In this study, through sports activity monitoring. it was possible to classify accurate sports activities and to notify emergency event such as falls. For further study, the accurate energy consumption algorithm for each sports activity is under development.

• Journal of Korea Multimedia Society
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• v.10 no.5
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• pp.559-568
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• 2007

Anchor-person scene detection is of significance for video shot semantic parsing and indexing clues extraction in content-based news video indexing and retrieval system. This paper proposes an efficient algorithm extracting anchor ranges that exist in sports news video for unit structuring of sports news. To detect anchor person scenes, first, anchor person candidate scene is decided by DCT coefficients and motion vector information in the MPEG4 compressed video. Then, from the candidate anchor scenes, image processing method is utilized to classify the news video into anchor-person scenes and non-anchor(sports) scenes. The proposed scheme achieves a mean precision and recall of 98% in the anchor-person scenes detection experiment.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.246-256
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• 2022

Recently, parents and teachers consider physical education as a minor subject for students in elementary and secondary schools. Physical education performance has become increasingly significant as parents and schools pay more attention to physical schooling. The sports mining with distribution analysis model considers different factors, including the games, comments, conversations, and connection made on numerous sports interests. Using different machine learning/deep learning approach, children's athletic and academic interests can be tracked over the course of their academic lives. There have been a number of studies that have focused on predicting the success of students in higher education. Sports interest prediction research at the secondary level is uncommon, but the secondary level is often used as a benchmark to describe students' educational development at higher levels. An Automated Student Interest Prediction on Sports Mining using DL Based Bi-directional Long Short-Term Memory model (BiLSTM) is presented in this article. Pre-processing of data, interest classification, and parameter tweaking are all the essential operations of the proposed model. Initially, data augmentation is used to expand the dataset's size. Secondly, a BiLSTM model is used to predict and classify user interests. Adagrad optimizer is employed for hyperparameter optimization. In order to test the model's performance, a dataset is used and the results are analysed using precision, recall, accuracy and F-measure. The proposed model achieved 95% accuracy on 400^th instances, where the existing techniques achieved 93.20% accuracy for the same. The proposed model achieved 95% of accuracy and precision for 60%-40% data, where the existing models achieved 93% for accuracy and precision.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.152-161
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• 2011

This study examines characteristics of sports movies between Korea and the U.S. Especially, this paper focuses on three different characteristics including structural, internal characteristics and performance of sports movies. The major results are as follows: firstly, there were similarities between two countries in terms of opening year, genre, classification but movie subjects. More specifically, movie opening years were not related to global sports events. In addition, most sports movies were mainly produced by human genre and all age movie classification. However, in terms of sports subjects, although Korean movies were constructed by baseball game, the U.S. movies were based on football game. Secondly, there were also similarities between both sports movies in terms of external characteristics such as gender difference of hero or heroine, internal story structure and their story sources. Most of sports movies have used real stories as movie story. Finally, although many of the U.S. sports movies recorded the high degree of expenditures, three movies recorded the high degree of movie consumption.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.513-519
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• 2022

The 4th industrial revolution has made our life convenient, and the development of new technologies has brought affluent life. Considering that the 4th industrial revolution is a next-generation industrial revolution led by robot technology, life science, and artificial intelligence, the need for convergence research in sports field stimulated the continuous efforts for the academic foundation of sports. Recently, as interest in home training has increased due to the COVID-19 pandemic, the Pilates has also attracted much attentions demand for related supplies has increased. This is an demonstration that COVID-19 has had an impact on the sports industry ecosystem. The purpose of this study was, accordingly, to identify current trends through accurate patent information related to Pilates and to provide basic data for future studies on sports convergence industry and sports intellectual property. For the purpose, based on the data from Jan. 1 to Dec. 31, 2021 provided by KIPRIS (www.kipris.or.kr), the patent information search service provided by the Korean Intellectual Property Office, patent status analysis, international patent classification (IPC) patent analysis, and detailed patent analysis by classification were performed.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.109-112
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• 2001

This paper proposes a method to classify different video contents using features of digital video. Classified video types are the news, drama, show, sports, and talk program. Features, such as intra-coded macroblock number St motion vector in P-picture in MPEG domain are used. The frame difference of YCbCr is also employed as a measure of classification. We detect the occurrences of cuts in a video for a measure of classification. Finally, back-propagation neural-network of 3 layers is used to classify video contents.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.897-904
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• 2022

Sports video is a very critical information resource. High-precision extraction of effective segments in sports video can better assist coaches in analyzing the player's actions in the video, and enable users to more intuitively appreciate the player's hitting action. Aiming at the shortcomings of the current sports video clip extraction results, such as strong subjectivity, large workload and low efficiency, a classification method of sports video clips based on MobileNetV3 is proposed to save user time. Experiments evaluate the effectiveness of effective segment extraction. Among the extracted segments, the effective proportion is 97.0%, indicating that the effective segment extraction results are good, and it can lay the foundation for the construction of the subsequent badminton action metadata video dataset.

• Journal of the Korean Society of Costume
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• v.52 no.2
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• pp.139-150
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• 2002

The purpose of this study was to explore the effects of meteorological factors on sales of apparel products. Basic fiat came out daily meteorological data and sales data of apparel products in department store from 1998 to 2000. Four factors(the average temperature, rainfall, wind velocity, sunshine duration) from the nine meteorological factors were selected and were collected with Korea Meteorological Administration. Sales data were collected with business strategy department of H (department store in Seoul. The sales data were divided into six classifications, which are woman's wear, men's wear, children's wear, golf wear, sports wear, and inner wear. The results of this study were as follows: 1) Sales of apparel products were significantly correlated with the average temperature, rainfall, wind velocity, sunshine duration. Among the meteorological factors, temperature turned out to be the most influential in apparel sales and then the amount of rainfall, sunshine duration affected sales according to apparel classifications differently. 2) There were some differences among the apparel classifications in the effect of meteorological factors on the sales of apparel. In the spring. the higher the temperature was, the higher the sales of women's wear and golf wear were, but the lower the sales of children's wear, sports wear and inner wear were. In the summer, The higher the amount of rainfall was, the lower the sales of all the apparel classification were. The higher the temperature was, the higher the sales of sports wear were. In the fall, the lower the temperature was, the higher the sales of all the apparel classification except snorts wear were. In the winter, the meteorological factors had little effect on the sales of women's wear, men's wear and children's wear. The higher the temperature was, the higher the sales of golf wear were. The lower the temperature was, the higher the sales of sports wear were.

• Journal of the Korean Applied Science and Technology
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• v.40 no.1
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• pp.24-34
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• 2023

This study sought to explore the online consumption propensity of sports goods by generation of the productive population and to provide basic data to predict the future consumption market by segmenting online consumers in the sports 4.0 market. Therefore, this survey was conducted on those who consumed sports goods among the generation-specific groups (Generation Y and above, Z) of the productive population, and a total of 478 people's data were applied to the final analysis. Data processing was conducted with SPSS statistics (ver.21.0), frequency analysis, exploratory factor analysis, correlation analysis of re-examination reliability, reliability analysis, and decision tree analysis. According to the online consumption propensity of sports goods by generation of the productive population, there is a high probability of being classified as Generation Z group if the factors of leisure, joy, and environment are high. In addition, the classification accuracy of such a model was 69.7%.