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20 pages, 77185 KiB

Open AccessArticle

Classification of Tennis Shots with a Neural Network Approach

by Andreas Ganser, Bernhard Hollaus and Sebastian Stabinger

Sensors 2021, 21(17), 5703; https://doi.org/10.3390/s21175703 - 24 Aug 2021

Cited by 30 | Viewed by 6325

Data analysis plays an increasingly valuable role in sports. The better the data that is analysed, the more concise training methods that can be chosen. Several solutions already exist for this purpose in the tennis industry; however, none of them combine data generation with a wristband and classification with a deep convolutional neural network (CNN). In this article, we demonstrate the development of a reliable shot detection trigger and a deep neural network that classifies tennis shots into three and five shot types. We generate a dataset for the training of neural networks with the help of a sensor wristband, which recorded 11 signals, including an inertial measurement unit (IMU). The final dataset included 5682 labelled shots of 16 players of age 13–70 years, predominantly at an amateur level. Two state-of-the-art architectures for time series classification (TSC) are compared, namely a fully convolutional network (FCN) and a residual network (ResNet). Recent advances in the field of machine learning, like the Mish activation function and the Ranger optimizer, are utilized. Training with the rather inhomogeneous dataset led to an F₁ score of 96% in classification of the main shots and 94% for the expansion. Consequently, the study yielded a solid base for more complex tennis analysis tools, such as the indication of success rates per shot type. Full article

(This article belongs to the Special Issue Activity Recognition Using Constrained IoT Devices)

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14 pages, 1819 KiB

Open AccessArticle

Enhanced Video Classification System Using a Block-Based Motion Vector

by Jayasree K and Sumam Mary Idicula

Information 2020, 11(11), 499; https://doi.org/10.3390/info11110499 - 24 Oct 2020

Cited by 2 | Viewed by 2644

Abstract

The main objective of this work was to design and implement a support vector machine-based classification system to classify video data into predefined classes. Video data has to be structured and indexed for any video classification methodology. Video structure analysis involves shot boundary detection and keyframe extraction. Shot boundary detection is performed using a two-pass block-based adaptive threshold method. The seek spread strategy is used for keyframe extraction. In most of the video classification methods, selection of features is important. The selected features contribute to the efficiency of the classification system. It is very hard to find out which combination of features is most effective. Feature selection makes relevance to the proposed system. Herein, a support vector machine-based classifier was considered for the classification of video clips. The performance of the proposed system considered six categories of video clips: cartoons, commercials, cricket, football, tennis, and news. When shot level features and keyframe features, along with motion vectors, were used, 86% correct classification was achieved, which was comparable with the existing methods. The research concentrated on feature extraction where combination of selected features was given to a classifier to get the best classification performance. Full article

(This article belongs to the Section Information Processes)

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