Classification of 40 Different Human Movements with CNN Architectures and Comparison of Their Performance

Abstract

Detection of human movements has become one of the current issues with the developing technology. Recognition of human movements is used in many areas such as security systems, human computer interaction, human robot interaction. Due to the increase in data stored in databases, deep learning methods have recently become one of the most frequently used methods. At this study, it is aimed to classify human movements by using Convolutional Neural Network (CNN) architectures. Images are classified with InceptionV3, Googlenet and Alexnet architectures using a data set with 40 different motion classes. The highest accuracy rate with 76.15% was obtained in InceptionV3 architecture. Increasing the amount of data in CNN networks is a parameter that closely concerns the network uptime. Since 40 different motion classes are used in this study, the results obtained in the related architectures are obtained in different times.

Keywords

• Deep Learning
• Human movements
• Alexnet
• Googlenet
• Inceptionv3

References

1. [1] Ebrahim, M., Al-Ayyoub, M., & Alsmirat, M. A. (2019, June). Will Transfer Learning Enhance ImageNet Classification Accuracy Using ImageNet-Pretrained Models?. In 2019 10th International Conference on Information and Communication Systems (ICICS) (pp. 211-216). IEEE. DOI: 10.1109/IACS.2019.8809114
2. [2] AYDIN, İ., & AŞICI, B. (2020). İnsan Hareketlerinin Tanınması için Parçacık Sürü Optimizasyonu Tabanlı Topluluk Sınıflandırıcı Yöntemi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(2), 381-390. https://doi.org/10.35234/fumbd.671403
3. [3] Murad, A., & Pyun, J. Y. (2017). Deep recurrent neural networks for human activity recognition. Sensors, 17(11), 2556. https://doi.org/10.3390/s17112556
4. [4] Batool, M., Jalal, A., & Kim, K. (2019, August). Sensors Technologies for Human Activity Analysis Based on SVM Optimized by PSO Algorithm. In 2019 International Conference on Applied and Engineering Mathematics (ICAEM) (pp. 145-150). IEEE. DOI: 10.1109/ICAEM.2019.8853770
5. [5] Altun, K., & Barshan, B. (2010, August). Human activity recognition using inertial/magnetic sensor units. In International workshop on human behavior understanding (pp. 38-51). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14715-9_5
6. [6] Ke, S. R., Thuc, H. L. U., Lee, Y. J., Hwang, J. N., Yoo, J. H., & Choi, K. H. (2013). A review on video-based human activity recognition. Computers, 2(2), 88-131. https://doi.org/10.3390/computers2020088
7. [7] Yildirim, M., Çinar, A. (2019). Classification of white blood cells by deep learning methods for diagnosing disease. Revue d'Intelligence Artificielle, Vol. 33, No. 5, pp. 335-340. https://doi.org/10.18280/ria.330502
8. [8] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).

9. [9] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
10. [10] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
11. [11] Stanford University, http://vision.stanford.edu/Datasets/40actions.html
12. [12] Özkan, İ. N. İ. K., & Ülker, E. (2017). Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104.
13. [13] YILDIZ, O. (2019). Derin öğrenme yöntemleriyle dermoskopi görüntülerinden melanom tespiti: Kapsamlı bir çalışma. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 34(4), 2241-2260. https://doi.org/10.17341/gazimmfd.435217
14. [14] Lin, C., Li, L., Luo, W., Wang, K. C., & Guo, J. (2019). Transfer learning based traffic sign recognition using inception-v3 model. Periodica Polytechnica Transportation Engineering, 47(3), 242-250. https://doi.org/10.3311/PPtr.11480
15. [15] Çinar, A., Yildirim, M. (2020). Classification of malaria cell images with deep learning architectures. Ingénierie des Systèmes d’Information, Vol. 25, No. 1, pp. 35-39. https://doi.org/10.18280/isi.250105
16. [16] Yildirim, M., Cinar, A. (2020). A deep learning based hybrid approach for COVID-19 disease detections. Traitement du Signal, 37(3): 461-468. https://doi.org/10.18280/ts.370313
17. [17] Pei, J.Y., Shan, P. (2019). A micro-expression recognition algorithm for students in classroom learning based on convolutional neural network. Traitement du Signal, Vol. 36, No. 6, pp. 557-563. https://doi.org/10.18280/ts.360611
18. [18] Jiang, X., Chang, L., & Zhang, Y. D. (2020). Classification of Alzheimer’s disease via eight-layer convolutional neural network with batch normalization and dropout techniques. Journal of Medical Imaging and Health Informatics, 10(5), 1040-1048. https://doi.org/10.1166/jmihi.2020.3001
19. [19] Öztürk, Ş., Yigit, E., & Özkaya, U. Fused Deep Features Based Classification Framework for Covid-19 Classification with Optimized MLP. Konya Mühendislik Bilimleri Dergisi, 8, 15-27. https://doi.org/10.36306/konjes.821782
20. [20] Çinar, A., Yıldırım, M. (2020). Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Medical Hypotheses, 139: 109684. https://doi.org/10.1016/j.mehy.2020.109684
21. [21] Kanda, Y., Sasaki, K. S., Ohzawa, I., & Tamura, H. (2020). Deleting object selective units in a fully-connected layer of deep convolutional networks improves classification performance. arXiv preprint arXiv:2001.07811.
22. [22] Kadam, V. J., Jadhav, S. M., & Vijayakumar, K. (2019). Breast cancer diagnosis using feature ensemble learning based on stacked sparse autoencoders and softmax regression. Journal of medical systems, 43(8), 1-11. https://doi.org/10.1007/s10916-019-1397-z
23. [23] Zeng, G. (2020). On the confusion matrix in credit scoring and its analytical properties. Communications in Statistics-Theory and Methods, 49(9), 2080-2093.https://doi.org/10.1080/03610926.2019.1568485