{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:30:28Z","timestamp":1778693428300,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,18]],"date-time":"2021-04-18T00:00:00Z","timestamp":1618704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recognizing the sport of cricket on the basis of different batting shots can be a significant part of context-based advertisement to users watching cricket, generating sensor-based commentary systems and coaching assistants. Due to the similarity between different batting shots, manual feature extraction from video frames is tedious. This paper proposes a hybrid deep-neural-network architecture for classifying 10 different cricket batting shots from offline videos. We composed a novel dataset, CricShot10, comprising uneven lengths of batting shots and unpredictable illumination conditions. Impelled by the enormous success of deep-learning models, we utilized a convolutional neural network (CNN) for automatic feature extraction, and a gated recurrent unit (GRU) to deal with long temporal dependency. Initially, conventional CNN and dilated CNN-based architectures were developed. Following that, different transfer-learning models were investigated\u2014namely, VGG16, InceptionV3, Xception, and DenseNet169\u2014which freeze all the layers. Experiment results demonstrated that the VGG16\u2013GRU model outperformed the other models by attaining 86% accuracy. We further explored VGG16 and two models were developed, one by freezing all but the final 4 VGG16 layers, and another by freezing all but the final 8 VGG16 layers. On our CricShot10 dataset, these two models were 93% accurate. These results verify the effectiveness of our proposed architecture compared with other methods in terms of accuracy.<\/jats:p>","DOI":"10.3390\/s21082846","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T21:59:49Z","timestamp":1618869589000},"page":"2846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["CricShotClassify: An Approach to Classifying Batting Shots from Cricket Videos Using a Convolutional Neural Network and Gated Recurrent Unit"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1087-3740","authenticated-orcid":false,"given":"Anik","family":"Sen","sequence":"first","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Chittagong University of Engineering &amp; Technology (CUET), Chattogram 4349, Bangladesh"},{"name":"Department of Computer Science &amp; Engineering, Premier University, Chattogram 4000, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7345-0999","authenticated-orcid":false,"given":"Kaushik","family":"Deb","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Chittagong University of Engineering &amp; Technology (CUET), Chattogram 4349, Bangladesh"}]},{"given":"Pranab Kumar","family":"Dhar","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Chittagong University of Engineering &amp; Technology (CUET), Chattogram 4349, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8994-729X","authenticated-orcid":false,"given":"Takeshi","family":"Koshiba","sequence":"additional","affiliation":[{"name":"Faculty of Education and Integrated Arts and Sciences, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo 169-8050, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Russo, M.A., Filonenko, A., and Jo, K.H. 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