Scene Classification for Sports Video Summarization Using Transfer Learning
This paper proposes a novel method for sports video scene classification with the particular intention of video summarization. Creating and publishing a shorter version of the video is more interesting than a full version due to instant entertainment. Generating shorter summaries of the videos is a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/6/1702 |
| _version_ | 1798037710910259200 |
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| author | Muhammad Rafiq Ghazala Rafiq Rockson Agyeman Gyu Sang Choi Seong-Il Jin |
| author_facet | Muhammad Rafiq Ghazala Rafiq Rockson Agyeman Gyu Sang Choi Seong-Il Jin |
| author_sort | Muhammad Rafiq |
| collection | DOAJ |
| description | This paper proposes a novel method for sports video scene classification with the particular intention of video summarization. Creating and publishing a shorter version of the video is more interesting than a full version due to instant entertainment. Generating shorter summaries of the videos is a tedious task that requires significant labor hours and unnecessary machine occupation. Due to the growing demand for video summarization in marketing, advertising agencies, awareness videos, documentaries, and other interest groups, researchers are continuously proposing automation frameworks and novel schemes. Since the scene classification is a fundamental component of video summarization and video analysis, the quality of scene classification is particularly important. This article focuses on various practical implementation gaps over the existing techniques and presents a method to achieve high-quality of scene classification. We consider cricket as a case study and classify five scene categories, i.e., batting, bowling, boundary, crowd and close-up. We employ our model using pre-trained AlexNet Convolutional Neural Network (CNN) for scene classification. The proposed method employs new, fully connected layers in an encoder fashion. We employ data augmentation to achieve a high accuracy of 99.26% over a smaller dataset. We conduct a performance comparison against baseline approaches to prove the superiority of the method as well as state-of-the-art models. We evaluate our performance results on cricket videos and compare various deep-learning models, i.e., Inception V3, Visual Geometry Group (VGGNet16, VGGNet19), Residual Network (ResNet50), and AlexNet. Our experiments demonstrate that our method with AlexNet CNN produces better results than existing proposals. |
| first_indexed | 2024-04-11T21:30:16Z |
| format | Article |
| id | doaj.art-5ac1cbf513ca47d9b6798e313dfd22e6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:30:16Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5ac1cbf513ca47d9b6798e313dfd22e62022-12-22T04:02:01ZengMDPI AGSensors1424-82202020-03-01206170210.3390/s20061702s20061702Scene Classification for Sports Video Summarization Using Transfer LearningMuhammad Rafiq0Ghazala Rafiq1Rockson Agyeman2Gyu Sang Choi3Seong-Il Jin4Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, KoreaThe division of computer convergence, Chungnam National University, Daejeon 34134, KoreaThis paper proposes a novel method for sports video scene classification with the particular intention of video summarization. Creating and publishing a shorter version of the video is more interesting than a full version due to instant entertainment. Generating shorter summaries of the videos is a tedious task that requires significant labor hours and unnecessary machine occupation. Due to the growing demand for video summarization in marketing, advertising agencies, awareness videos, documentaries, and other interest groups, researchers are continuously proposing automation frameworks and novel schemes. Since the scene classification is a fundamental component of video summarization and video analysis, the quality of scene classification is particularly important. This article focuses on various practical implementation gaps over the existing techniques and presents a method to achieve high-quality of scene classification. We consider cricket as a case study and classify five scene categories, i.e., batting, bowling, boundary, crowd and close-up. We employ our model using pre-trained AlexNet Convolutional Neural Network (CNN) for scene classification. The proposed method employs new, fully connected layers in an encoder fashion. We employ data augmentation to achieve a high accuracy of 99.26% over a smaller dataset. We conduct a performance comparison against baseline approaches to prove the superiority of the method as well as state-of-the-art models. We evaluate our performance results on cricket videos and compare various deep-learning models, i.e., Inception V3, Visual Geometry Group (VGGNet16, VGGNet19), Residual Network (ResNet50), and AlexNet. Our experiments demonstrate that our method with AlexNet CNN produces better results than existing proposals.https://www.mdpi.com/1424-8220/20/6/1702deep learningalexnet cnnsmall datasetdata augmentation |
| spellingShingle | Muhammad Rafiq Ghazala Rafiq Rockson Agyeman Gyu Sang Choi Seong-Il Jin Scene Classification for Sports Video Summarization Using Transfer Learning Sensors deep learning alexnet cnn small dataset data augmentation |
| title | Scene Classification for Sports Video Summarization Using Transfer Learning |
| title_full | Scene Classification for Sports Video Summarization Using Transfer Learning |
| title_fullStr | Scene Classification for Sports Video Summarization Using Transfer Learning |
| title_full_unstemmed | Scene Classification for Sports Video Summarization Using Transfer Learning |
| title_short | Scene Classification for Sports Video Summarization Using Transfer Learning |
| title_sort | scene classification for sports video summarization using transfer learning |
| topic | deep learning alexnet cnn small dataset data augmentation |
| url | https://www.mdpi.com/1424-8220/20/6/1702 |
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