Araştırma Makalesi
Dış Ortam Görüntülerindeki İnsan Hareketlerinin Hibrit Derin Öğrenme Yöntemleri Kullanarak Sınıflandırılması
Öz
Bu makale
çalışmasında, dış ortam görüntülerinde yer alan insan hareketlerinin otomatik
sınıflandırılması için hibrit bir derin öğrenme yaklaşımı önerilmiştir. İlk
olarak, dış ortamdan çekilen görüntü içerisindeki kişilerin tespiti
sağlanmıştır. Bu amaçla, literatürde yaygın olarak kullanılan önceden eğitilmiş
derin nesne tespit aracı olan YOLO kullanılmıştır. Dış ortam görüntülerinin
elde edilmesinde Google street view platformu tercih edilmiştir. Daha sonra
tespit edilen kişiler için hareket sınıfları oluşturulmuştur. Bu hareket
sınıfları; sağa yürüme, sola yürüme, ayakta durma ve oturma şeklindedir.
Böylece dış ortam görüntülerinden tespit edilen kişiler için kapsamlı bir veri
seti oluşturulmuştur. Sınıfları belirlenen verilerin otomatik olarak tanınması
işlemi için bir konvolüsyonel sinir ağı (KSA) modeli tasarlanmıştır. Eğitimi
tamamlanan bu model, YOLO nesne tespit sistemi ile hibrit bir şekilde
kullanılarak giriş görüntüsü içerisindeki kişi hareketlerinin otomatik olarak
tanınmasını sağlamıştır. Makale kapsamında, dört sınıflı bir veri seti
oluşturularak önerilen sistemin performans değerlendirmeleri yapılmıştır.
Anahtar Kelimeler
Derin öğrenme İnsan hareket tespiti Görüntü işleme Nesne tespiti
Kaynakça
- 1. Dhamsania, C. J., & Ratanpara, T. V. (2016). A survey on Human action recognition from videos. Online International Conference on In Green Engineering and Technologies, 1-5. 2. Geronimo, D., Lopez, A. M., Sappa, A. D., & Graf, T. (2010). Survey of pedestrian detection for advanced driver assistance systems. IEEE transactions on pattern analysis and machine intelligence, 32(7): 1239-1258. 3. Ouyang, W., Zeng, X., & Wang, X. (2016). Learning mutual visibility relationship for pedestrian detection with a deep model. International Journal of Computer Vision, 120(1): 14-27. 4. Aguilar, W. G., Luna, M. A., Moya, J. F., Abad, V., Parra, H., & Ruiz, H. (2017). Pedestrian detection for UAVs using cascade classifiers with meanshift. In Semantic Computing (ICSC), 509-514. 5. Thomas B. Moeslund, Adrian Hilton, Volker Krüger, (2006). A survey of advances in vision-based human motion capture and analysis, Computer Vision and Image Understanding, 104 (2–3) 90–126. 6. Cheng, G., Wan, Y., Saudagar, A. N., Namuduri, K., & Buckles, B. P. (2015). Advances in human action recognition: A survey. arXiv preprint arXiv:1501.05964. 7. Aggarwal, J. K., & Ryoo, M. S. (2011). Human activity analysis: A review. ACM Computing Surveys, 43(3): 16. 8. Chaudhary, A., Raheja, J. L., Das, K., & Raheja, S. (2011). A survey on hand gesture recognition in context of soft computing. In International Conference on Computer Science and Information Technology 133: 46-55. 9. Weinland, D., Ronfard, R., Boyer, E., (2011). A survey of vision-based methods for action representation, segmentation and recognition. Computer Vision and Image Understanding 115: 224–241. 10. Yu, G., & Yuan, J. (2015). Fast action proposals for human action detection and search. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1302-1311. 11. Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., & Baskurt, A. (2011). Sequential deep learning for human action recognition. In International Workshop on Human Behavior Understanding, 7065: 29-39. 12. Eroglu, H., Gokce, C. O., & Ilk, H. G. (2014, April). Human action recognition via spatial and temporal methods. In Signal Processing and Communications Applications Conference (SIU), 2014 22nd, 104-107. 13. Niebles, J. C., Wang, H., & Fei-Fei, L. (2008). Unsupervised learning of human action categories using spatial-temporal words. International journal of computer vision, 79(3): 299-318. 14. Schuldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: a local SVM approach. In Pattern Recognition, 3: 32-36. 15. Ji, S., Xu, W., Yang, M., & Yu, K. (2013). 3D convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence, 35(1): 221-231. 16. Rahmani, H., Mian, A., & Shah, M. (2017). Learning a deep model for human action recognition from novel viewpoints. IEEE Transactions On Pattern Analysis and Machine Intelligence, 40(3), 667-681. 17. Simonyan, K., & Zisserman, A. (2014). Two-stream convolutional networks for action recognition in videos. In Advances In Neural Information Processing Systems, 27: 68-576. 18. İnternet Erişimi (2018): http://fortune.com/ai-artificial-intelligence-deep-machine-learning/ 19. Coşkun, M., Yildirim, Ö., Uçar, A. & Demir, Y. (2017). An Overview of Popular Deep Learning Methods. European Journal of Technique, 7(2): 165-176. 20. Gehring, J., Miao, Y., Metze, F., & Waibel, A. (2013). Extracting deep bottleneck features using stacked auto-encoders. In Acoustics, Speech and Signal Processing (ICASSP), 3377-3381. 21. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788. 22. Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. arXiv preprint. 23. Koçer, S., & Akdağ, A. (2017). Convolutional neural network based handgun detection. In Computer Science and Engineering (UBMK), 89-93. 24. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11): 2278-2324. 25. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25: 1097-1105. 26. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. Computer Vision and Pattern Recognition, 1-9. 27. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 28. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Computer Vision and Pattern Recognition, 770-778. 29. Yildirim Ö. (2018). A novel wavelet sequences based on deep bidirectional LSTM network model for ECG signal classification; Computers in Biology and Medicine; 2018; 96: 189 – 202. 30. Yildirim, Ö., San Tan, R., & Acharya, U. R. (2018). An efficient compression of ECG signals using deep convolutional autoencoders. Cognitive Systems Research, 52:198-211. 31. Uçar, A., Demir, Y., & Güzeliş, C. (2017). Object recognition and detection with deep learning for autonomous driving applications. Simulation, 93(9), 759-769.
Yıl 2018,
Cilt: 30 Sayı: 3, 121 - 129, 20.09.2018
Öz
Kaynakça
- 1. Dhamsania, C. J., & Ratanpara, T. V. (2016). A survey on Human action recognition from videos. Online International Conference on In Green Engineering and Technologies, 1-5. 2. Geronimo, D., Lopez, A. M., Sappa, A. D., & Graf, T. (2010). Survey of pedestrian detection for advanced driver assistance systems. IEEE transactions on pattern analysis and machine intelligence, 32(7): 1239-1258. 3. Ouyang, W., Zeng, X., & Wang, X. (2016). Learning mutual visibility relationship for pedestrian detection with a deep model. International Journal of Computer Vision, 120(1): 14-27. 4. Aguilar, W. G., Luna, M. A., Moya, J. F., Abad, V., Parra, H., & Ruiz, H. (2017). Pedestrian detection for UAVs using cascade classifiers with meanshift. In Semantic Computing (ICSC), 509-514. 5. Thomas B. Moeslund, Adrian Hilton, Volker Krüger, (2006). A survey of advances in vision-based human motion capture and analysis, Computer Vision and Image Understanding, 104 (2–3) 90–126. 6. Cheng, G., Wan, Y., Saudagar, A. N., Namuduri, K., & Buckles, B. P. (2015). Advances in human action recognition: A survey. arXiv preprint arXiv:1501.05964. 7. Aggarwal, J. K., & Ryoo, M. S. (2011). Human activity analysis: A review. ACM Computing Surveys, 43(3): 16. 8. Chaudhary, A., Raheja, J. L., Das, K., & Raheja, S. (2011). A survey on hand gesture recognition in context of soft computing. In International Conference on Computer Science and Information Technology 133: 46-55. 9. Weinland, D., Ronfard, R., Boyer, E., (2011). A survey of vision-based methods for action representation, segmentation and recognition. Computer Vision and Image Understanding 115: 224–241. 10. Yu, G., & Yuan, J. (2015). Fast action proposals for human action detection and search. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1302-1311. 11. Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., & Baskurt, A. (2011). Sequential deep learning for human action recognition. In International Workshop on Human Behavior Understanding, 7065: 29-39. 12. Eroglu, H., Gokce, C. O., & Ilk, H. G. (2014, April). Human action recognition via spatial and temporal methods. In Signal Processing and Communications Applications Conference (SIU), 2014 22nd, 104-107. 13. Niebles, J. C., Wang, H., & Fei-Fei, L. (2008). Unsupervised learning of human action categories using spatial-temporal words. International journal of computer vision, 79(3): 299-318. 14. Schuldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: a local SVM approach. In Pattern Recognition, 3: 32-36. 15. Ji, S., Xu, W., Yang, M., & Yu, K. (2013). 3D convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence, 35(1): 221-231. 16. Rahmani, H., Mian, A., & Shah, M. (2017). Learning a deep model for human action recognition from novel viewpoints. IEEE Transactions On Pattern Analysis and Machine Intelligence, 40(3), 667-681. 17. Simonyan, K., & Zisserman, A. (2014). Two-stream convolutional networks for action recognition in videos. In Advances In Neural Information Processing Systems, 27: 68-576. 18. İnternet Erişimi (2018): http://fortune.com/ai-artificial-intelligence-deep-machine-learning/ 19. Coşkun, M., Yildirim, Ö., Uçar, A. & Demir, Y. (2017). An Overview of Popular Deep Learning Methods. European Journal of Technique, 7(2): 165-176. 20. Gehring, J., Miao, Y., Metze, F., & Waibel, A. (2013). Extracting deep bottleneck features using stacked auto-encoders. In Acoustics, Speech and Signal Processing (ICASSP), 3377-3381. 21. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788. 22. Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. arXiv preprint. 23. Koçer, S., & Akdağ, A. (2017). Convolutional neural network based handgun detection. In Computer Science and Engineering (UBMK), 89-93. 24. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11): 2278-2324. 25. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25: 1097-1105. 26. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. Computer Vision and Pattern Recognition, 1-9. 27. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 28. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Computer Vision and Pattern Recognition, 770-778. 29. Yildirim Ö. (2018). A novel wavelet sequences based on deep bidirectional LSTM network model for ECG signal classification; Computers in Biology and Medicine; 2018; 96: 189 – 202. 30. Yildirim, Ö., San Tan, R., & Acharya, U. R. (2018). An efficient compression of ECG signals using deep convolutional autoencoders. Cognitive Systems Research, 52:198-211. 31. Uçar, A., Demir, Y., & Güzeliş, C. (2017). Object recognition and detection with deep learning for autonomous driving applications. Simulation, 93(9), 759-769.
Toplam 1 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | MBD |
| Yazarlar | |
| Yayımlanma Tarihi | 20 Eylül 2018 |
| Gönderilme Tarihi | 23 Mart 2018 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 30 Sayı: 3 |