Classification of Turkish Folk Dances using Deep Learning



Deep learning, ResNet50, Turkish folk dances, VGG16, CNN


The folk dances, which reflect the cultural values of the society consist of regular and continuous activities performed singly or in groups, accompanied by music. Folk dances show differences according to climate, geographical position and socio-economic status of the society. Classification of Turkish folk dances is an interesting subject due to the different hand, arm and foot postures of the dancers. The complexity of the background, camera angle, special clothing can cause the algorithms to give incorrect results. It gives a playground to experiment with different deep learning techniques. In this study, the classification of Turkish folk dances is carried out from video images with deep learning methods. Zeybek, Çiftetelli, Horon and Halay dances which are among Turkish folk dances were selected. The dataset consists of 24000 images in total, prepared as 6000 images belonging to each class (Halay, Zeybek, Çiftetelli, Horon). 75% of the images make up the training set and the validation set is 25% of the images. CNN model and pre-trained VGG16 and ResNet50 architectures with transfer learning technique are used in the classification of Turkish folk dances in video images. The models have been tested with YouTube data. The accuracy rates of the proposed CNN model, VGG16 and ResNet50 architectures are 94%, 97% and 98%, respectively.


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T. Eroğlu, Doğu ve Güneydoğu anadolu'da halk oyunları ve halayların incelenmesi, vol. 1, Ankara: Kılıçaslan matbaacılık, 1995.

S. Aydın, "Halk Oyunları," Kültür Turizm Bakanlığı, Ankara, 2009-Kasım.

Vanitha, D. D. . (2022). Comparative Analysis of Power switches MOFET and IGBT Used in Power Applications. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(5), 01–09.

Aparna Mohanty, Pratik Vaishnav, Prerana Jana, Anubhab Majumdar, Alfaz Ahmed, Trishita Goswami and Rajiv R. Sahay, "Nrityabodha: Towards understanding Indian classical dance using a deep learning approach," Signal Processing: Image Communication, p. 529–548, 2016.

K. Fırıldak and M. F. Talu, "Evrişimsel Sinir Ağlarında Kullanılan Transfer Öğrenme Yaklaşımlarının İncelenmesi," Anatolian Journal of Computer Science, vol. 4, no. 2, pp. 88-95, 2019.

Ş. Kılıç, "Derin öğrenme yöntemleri kullanılarak giyilebilir sensörlerden kişi tanıma," Ankara Üniversitesi Fen Bilimleri Enstitüsü, Ankara, 2021.

Bulla, P. . “Traffic Sign Detection and Recognition Based on Convolutional Neural Network”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 4, Apr. 2022, pp. 43-53, doi:10.17762/ijritcc.v10i4.5533.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg and L. Fei-Fei, "ImageNet Large Scale Visual Recognition Challenge," Int J Comput Vis, pp. 211-252, 2015.

Weilong Yang, Yang Wang and Greg Mori, "Recognizing Human Actions from Still Images with Latent Poses," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Canada, 2010.

J. K. Aggarwal and M. S. Ryoo, "Human activity analysis: A review," ACM Comput. Surv., vol. 43, no. 3, 2011.

J. K. aggarwal and Q. Cai, "Human motion analysis: A review," Comput. Vision Image Understand, pp. 428-440, 1999.

Bangpeng Yao , Aditya Khosla and Li Fei-Fei, "Classifying Actions and Measuring Action Similarity by Modeling the Mutual Context of Objects and Human Poses," in Proc. 28th Int. Conf. Mach. Learn. ICML , 2011.

Soumitra Samanta, Pulak Purkait and Bhabatosh Chanda, "Indian Classical Dance classification by learning dance pose bases," in IEEE Workshop on Applications of Computer Vision (WACV), USA, 2012.

Ioannis Kapsouras, Stylianos Karanikolos, Nikolaos Nikolaidis and Anastasios Tefas, "Folk dance recognition using a bag of words approach and ISA/STIP features," in BCI '13: Proceedings of the 6th Balkan Conference in Informatics, Greece, 2013.

Ankita Bisht, Riya Bora, Goutam Saini, Pushkar Shukla and Balasubrmanian Raman, "Indian Dance Form Recognition from Videos," in International IEEE Conference on Signal-Image Technologies and Internet-Based System, Jaipur, India, 2017.

Agarwal, D. A. . (2022). Advancing Privacy and Security of Internet of Things to Find Integrated Solutions. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 05–08.

K. V. V. Kumar, P. V. V. Kishore and D. Anil Kumar, "Indian Classical Dance Classification with Adaboost Multiclass Classifier on Multifeature Fusion," Mathematical Problems in Engineering, 2017.

K. V. V. Kumar and P. V. V. Kishore, "Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier," Smart Computing and Informatics, vol. 77, pp. 659-668, 2018.

P. V. V. Kishore, K. V. V. Kumar, E. Kiran Kumar, A. S. C. S. Sastry, M. Teja Kiran, D. Anil Kumar and M. V. D. Prasad, "Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks," Advances in Multimedia, 2018.

Swati Dewan, Shubham Agarwal and Navjyoti Singh, "A deep learning pipeline for Indian dance style classification," in Tenth International Conference on Machine Vision (ICMV 2017), Vienna, Austria, 2018.

Agarwal, A. . (2022). Symmetric, e-Projective Topoi of Non-Solvable, Trivially Fourier Random Variables and Selberg’s Conjecture. International Journal on Recent Trends in Life Science and Mathematics, 9(1), 01–10.

T. a. D. P. a. M. A. Mallick, "Posture and sequence recognition for Bharatanatyam dance performances using machine learning approach," Preprint, 2019.

Kabisha, M. S., Rahim, K. A., Khaliluzzaman, M., & Khan, S. I. (2022). Face and Hand Gesture Recognition Based Person Identification System using Convolutional Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 105–115.

Nikita Jain, Vibhuti Bansal , Deepali Virmani , Vedika Gupta, Lorenzo Salas-Morera and Laura Garcia-Hernandez, "An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms," Applied Sciences, vol. 11, no. 14, 2021.

N. A. Libre. (2021). A Discussion Platform for Enhancing Students Interaction in the Online Education. Journal of Online Engineering Education, 12(2), 07–12. Retrieved from

D. Bhavana, K. Kishore Kumar, Medasani Bipin Chandra, P.V. Sai Krishna Bhargav, D. Joy Sanjana and G. Mohan Gopi, "Hand Sign Recognition using CNN," International Journal of Performance Analysis in Sport, vol. 17, no. 3, pp. 314-321, 2021.

 Classic CNN architecture




How to Cite

H. . Nazari and S. . Kaynak, “Classification of Turkish Folk Dances using Deep Learning ”, Int J Intell Syst Appl Eng, vol. 10, no. 3, pp. 226–232, Oct. 2022.



Research Article