Novel automated detection of sports activities using shadow videos
Article
Barua, Prabal Datta, Tuncer, Turker, Dogan, Sengul, Ooi, Chui Ping and Acharya, Rajendra U.. 2024. "Novel automated detection of sports activities using shadow videos." Multimedia Tools and Applications. 83 (15), pp. 44933-44954. https://doi.org/10.1007/s11042-023-17407-1
| Article Title | Novel automated detection of sports activities using shadow videos |
|---|---|
| ERA Journal ID | 18083 |
| Article Category | Article |
| Authors | Barua, Prabal Datta, Tuncer, Turker, Dogan, Sengul, Ooi, Chui Ping and Acharya, Rajendra U. |
| Journal Title | Multimedia Tools and Applications |
| Journal Citation | 83 (15), pp. 44933-44954 |
| Number of Pages | 22 |
| Year | 2024 |
| Publisher | Springer |
| Place of Publication | United States |
| ISSN | 1380-7501 |
| 1573-7721 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s11042-023-17407-1 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s11042-023-17407-1 |
| 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-overlapping 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. |
| Keywords | Chi2 feature selection; Sports activities detection using shadow; Video processing; Transfer learning; LOSO validation |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | School of Business |
| Firat University, Turkey | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| School of Mathematics, Physics and Computing |
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