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Year 2021, Volume: 16 Issue: 1, 103 - 112, 15.03.2021

Abstract

References

  • [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] 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] 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] 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] 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] 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] 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] 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] 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] 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] Stanford University, http://vision.stanford.edu/Datasets/40actions.html
  • [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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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

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

Year 2021, Volume: 16 Issue: 1, 103 - 112, 15.03.2021

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.

References

  • [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] 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] 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] 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] 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] 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] 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] 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] 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] 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] Stanford University, http://vision.stanford.edu/Datasets/40actions.html
  • [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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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
There are 23 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section TJST
Authors

Muhammed Yıldırım 0000-0003-1866-4721

Ahmet Çınar 0000-0001-5528-2226

Publication Date March 15, 2021
Submission Date January 27, 2021
Published in Issue Year 2021 Volume: 16 Issue: 1

Cite

APA Yıldırım, M., & Çınar, A. (2021). Classification of 40 Different Human Movements with CNN Architectures and Comparison of Their Performance. Turkish Journal of Science and Technology, 16(1), 103-112.
AMA Yıldırım M, Çınar A. Classification of 40 Different Human Movements with CNN Architectures and Comparison of Their Performance. TJST. March 2021;16(1):103-112.
Chicago Yıldırım, Muhammed, and Ahmet Çınar. “Classification of 40 Different Human Movements With CNN Architectures and Comparison of Their Performance”. Turkish Journal of Science and Technology 16, no. 1 (March 2021): 103-12.
EndNote Yıldırım M, Çınar A (March 1, 2021) Classification of 40 Different Human Movements with CNN Architectures and Comparison of Their Performance. Turkish Journal of Science and Technology 16 1 103–112.
IEEE M. Yıldırım and A. Çınar, “Classification of 40 Different Human Movements with CNN Architectures and Comparison of Their Performance”, TJST, vol. 16, no. 1, pp. 103–112, 2021.
ISNAD Yıldırım, Muhammed - Çınar, Ahmet. “Classification of 40 Different Human Movements With CNN Architectures and Comparison of Their Performance”. Turkish Journal of Science and Technology 16/1 (March 2021), 103-112.
JAMA Yıldırım M, Çınar A. Classification of 40 Different Human Movements with CNN Architectures and Comparison of Their Performance. TJST. 2021;16:103–112.
MLA Yıldırım, Muhammed and Ahmet Çınar. “Classification of 40 Different Human Movements With CNN Architectures and Comparison of Their Performance”. Turkish Journal of Science and Technology, vol. 16, no. 1, 2021, pp. 103-12.
Vancouver Yıldırım M, Çınar A. Classification of 40 Different Human Movements with CNN Architectures and Comparison of Their Performance. TJST. 2021;16(1):103-12.