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Novel automated detection of sports activities using shadow videos

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Abstract

The aim of this work is to investigate the use of shadow videos for daily sports activity detection and to contribute to the emerging field of shadow-based classification in machine learning. A novel deep feature engineering model is proposed, and a new shadow video dataset is collected to validate the proposed model. Furthermore, the use of shadow videos ensures privacy protection for individuals. To evaluate the proposed system, a dataset comprising five sports activities (i.e., squat, steady run, standing butterfly, overhead side bend, and knee lift) recorded from 33 participants is used. The proposed model works in the following way: (i) videos are divided into frames and aggregated into non-overlap** blocks of six frames to create images, (ii) deep features are extracted from three fully connected layers of the pre-trained AlexNet, resulting in 4096, 4096, and 1000 features from fc6, fc7, and fc8 layers, respectively. These three feature vectors are then merged to generate a final feature vector with a length of 9192 (= 4096 + 4096 + 1000). (iii) The Chi2 selector is employed to select the most distinctive 1000 features in the feature selection phase, and (iv) the support vector machine (SVM) with leave-one-subject-out (LOSO) validation is used to classify the five sports activities. The proposed deep features coupled with Chi2 based model achieved a classification accuracy of 88.49% using the SVM classifier with LOSO cross-validation (CV) on our collected dataset.

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Funding

This study was supported by Firat University Scientific Research Projects with project number TEKF.22.06.

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Authors and Affiliations

  1. School of Business (Information System), University of Southern Queensland, Toowoomba, Australia

    Prabal Datta Barua

  2. Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

    Turker Tuncer & Sengul Dogan

  3. School of Science and Technology, Singapore University of Social Sciences, Singapore, 599494, Singapore

    Chui ** Ooi

  4. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia

    Rajendra U. Acharya

Authors
  1. Prabal Datta Barua
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  2. Turker Tuncer
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  3. Sengul Dogan
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  4. Chui ** Ooi
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  5. Rajendra U. Acharya
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All authors contributed equally to the study.

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Correspondence to Sengul Dogan.

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Barua, P.D., Tuncer, T., Dogan, S. et al. Novel automated detection of sports activities using shadow videos. Multimed Tools Appl 83, 44933–44954 (2024). https://doi.org/10.1007/s11042-023-17407-1

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