Sports video summarization using highlights and play-breaks

2003 ◽  
Author(s):  
Dian Tjondronegoro ◽  
Yi-Ping Phoebe Chen ◽  
Binh Pham
Keyword(s):  
Video Summarization ◽  
Sports Video

scholarly journals Scene Classification for Sports Video Summarization Using Transfer Learning

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1702 ◽  
Author(s):  
Muhammad Rafiq ◽  
Ghazala Rafiq ◽  
Rockson Agyeman ◽  
Gyu Sang Choi ◽  
Seong-Il Jin
Keyword(s):  
Data Augmentation ◽  
Video Summarization ◽  
Performance Comparison ◽  
Practical Implementation ◽  
Scene Classification ◽  
Advertising Agencies ◽  
Sports Video ◽  
Performance Results ◽  
Implementation Gaps

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.

Sports video summarization based on motion analysis

2013 ◽  
Vol 39 (3) ◽  
pp. 790-796 ◽  
Author(s):  
Engin Mendi ◽  
Hélio B. Clemente ◽  
Coskun Bayrak
Keyword(s):  
Motion Analysis ◽  
Video Summarization ◽  
Sports Video
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