Review on recent Computer Vision Methods for Human Action Recognition
Abstract The subject of human activity recognition is considered an important goal in the domain of computer vision from the beginning of its development and has reached new levels. It is also thought of as a simple procedure. Problems arise in fast-moving and advanced scenes, and the numerical analysis of artificial intelligence (AI) through activity prediction mistreatment increased the attention of researchers to study. Having decent methodological and content related variations, several datasets were created to address the evaluation of these ways. Human activities play an important role but with challenging characteristic in various fields. Many applications exist in this field, such as smart home, helpful AI, HCI (Human-Computer Interaction), advancements in protection in applications such as transportation, education, security, and medication management, including falling or helping elderly in medical drug consumption. The positive impact of deep learning techniques on many vision applications leads to deploying these ways in video processing. Analysis of human behavior activities involves major challenges when human presence is concerned. One individual can be represented in multiple video sequences through skeleton, motion and/or abstract characteristics. This work aims to address human presence by combining many options and utilizing a new RNN structure for activities. The paper focuses on recent advances in machine learning-assisted action recognition. Existing modern techniques for the recognition of actions and prediction similarly because the future scope for the analysis is mentioned accuracy within the review paper.
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Aggarwal, J.K. & Cai, Q. (1999). Human motion analysis: A review. Computer Vision and Image Understanding, 73, 428–440.
Albu, V. (2016). Measuring Customer Behavior with Deep Convolutional Neural Networks; BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 7(1), pp. 74–79.
Baldominos, A., Saez, Y. & Isasi, P. (2018). Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments. Sensors, 18(4), 1288.
Ballas, N., Yao, L., Pal, C. & Courville, A. (2015) . Delving Deeper into Convolutional Networks for Learning Video Reresentations. arXiv preprint arXiv:1511.06432.
Bojanowski, P., Lajugie, R., Bach, F., Laptev, I., Ponce, J., Schmid, C. & Sivic, J. (2014, September). Weakly supervised action labeling in videos under ordering constraints. In European Conference on Computer Vision, Springer, Cham, pp. 628–643.
Cao, L., Liu, Z. & Huang, T.S. (2010, June). Cross-dataset action detection. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp. 1998–2005.
Cippitelli, E., Gasparrini, S., Gambi, E. & Spinsante, S. (2016). A Human Activity Recognition System Using Skeleton Data from RGBD Sensors. Computational Intelligence and Neuroscience.
Cruz-Silva, J.E., Montiel-Pérez, J.Y. & Sossa-Azuela, H. (2019, October). 3-D Human Body Posture Reconstruction by Computer Vision. In Mexican International Conference on Artificial Intelligence, Springer, Cham, pp. 579–588.
Das, S., Koperski, M., Bremond, F. & Francesca, G. (2018). A fusion of appearance based CNNs and temporal evolution of skeleton with LSTM for daily live in action. Recognition ArXiv e-prints.
Delaitre, V., Laptev, I. & Sivic, J. (2010, August). Recognizing human actions in still images: a study of bag-of-features and part-based representations. In BMVC 2010-21st British Machine Vision Conference.
Faria, D.R., Premebida, C. & Nunes, U. (2014, August). A probabilistic approach for human everyday activities recognition using body motion from RGB-D images. In The 23rd IEEE International Symposium on Robot and Human Interactive Communication, IEEE, pp. 732–737.
Feichtenhofer, C., Pinz, A. & Zisserman, A. (2016). Convolutional twostream network fusion for video action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1933–1941.
Gaglio, S., Re, G.L. & Morana, M. (2014). Human Activity Recognition Process Using 3-D Posture Data. IEEE Transactions on Human-Machine Systems 45(5), 586–597.
Gao, Y., Xiang, X., Xiong, N., Huang, B., Lee, H.J., Alrifai, R., Jiang, X. & Fang, Z. (2018). Human Action Monitoring for Healthcare based on Deep Learning. IEEE Access, 6, 52277–52285.
Herath, S., Harandi, M. … Porikli, F. (2017). Going deeper into action recognition: A survey. Image and vision computing, 60, 4–21.
Idrees, H., Zamir, A. R., Jiang, Y. G., Gorban, A., Laptev, I., Sukthankar, R. … Shah, M. (2017). The THUMOS challenge on action recognition for videos ‘in the wild’. Computer Vision and Image Understanding, 155, 1–23.
Ijjina, E. P. & Krishna Mohan, C. (2016). Hybrid deep neural network model for human action recognition. Applied Soft Computing, 46, 936–952.
Jaouedi, N., Perales, F. J., Buades, J. M., Boujnah, N. & Bouhlel, M. S. (2020). Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model. Sensors, 20(17), 4944.
Ji, Y., Xu, F., Yang, Y., Shen, F., Shen, H. T. & Zheng, WS. (2018, October). A Large-scale RGB-D Database for Arbitrary-view Human Action Recognition. In Proceedings of the 26th ACM Multimedia Conference on Multimedia, pp. 1510–1518).
Khaire, P., Kumar, P. & Imran, J. (2018). Combining CNN Streams of RGB-D and Skeletal Data for Human Activity Recognition. Pattern Recognition Letters, 115, 107–116.
Kim, H. & Kim, I. (2015). Human Activity Recognition as Time-Series Analysis. Mathematical Problems in Engineering.
Kliper-Gross, O., Hassner, T. & Wolf, L. (2011). The action similarity labeling challenge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3), 615–621.
Koppula, H.S., Gupta, R. & Saxena, A. (2013). Learning human activities and object affordances from RGB-D videos. The International Journal of Robotics Research, 32(8), 951–970.
Krishna, R., Hata, K., Ren, F., Fei-Fei, L. & Carlos Niebles, J. (2017). Densecaptioning events in videos. In Proceedings of the IEEE International Conference on Computer Vision, pp. 706–715.
Latah, M. (2017). Human action recognition using support vector machines and 3D convolutional neural networks. Int. J. Adv. Intell. Informatics, 3(1), 47.
Liu, C., Hu, Y., Li, Y., Song, S. & Liu, J. (2017). PKU-MMD: a large scale benchmark for continuous multi-modal human action understanding. arXiv:1703.07475
Liu, J., Luo, J. & Shah, M. (2009, June). Recognizing realistic actions from video? in the Wild. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition , IEEE, pp. 1996–2003.
Ma, S., Bargal, S. A., Zhang, J., Sigal, L. & Sclaroff, S. (2017). Do less and achieve more: Training CNNs for action recognition utilizing action images from the Web. Pattern Recognition, 68, 334–345.
Manzi, A., Dario, P. & Cavallo, F. (2017). A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data. Sensors, 17(5), 1100.
Minnen, D., Westeyn, T., Starner, T., Ward, J.A. & Lukowicz, P. (2006). Performance metrics and evaluation issues for continuous activity recognition. Performance Metrics for Intelligent Systems ,4 , pp. 141–148.
Murad, A. & Ryun, J. Y. (2017). Deep Recurrent Neural Networks for Human Activity Recognition. Sensors, 17(11), 2556.
Ni, B., Pei, Y., Moulin, P. & Yan, S. (2013). Multilevel Depth and Image Fusion for Human Activity Detection. IEEE Transactions on Cybernetics, 43(5), 1383–1394.
Ohnishi, K., Kanehira, A., Kanezaki, A. & Harada, T. (2016). Recognizing activities of daily living with a wrist-mounted camera. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3103–3111.
Pham, H. H., Khoudour, L., Crouzil, A., Zegers, P. & Velastin Carroza, S. A. (2015). Video-based human action recognition using deep learning: a review.
Qin, Z., Zhang, Y., Meng, S., Qin, Z. & Choo, K. R. (2020). Imaging and fusing time series for wearable sensors based human activity recognition. Information Fusion, 53, 80–87.
Reddy, K. K. & Shah, M. (2013). Recognizing 50 human action categories of web videos. Machine Vision and Applications, 24(5), 971–981.
Rodríguez-Moreno, I., Martínez-Otzeta, J. M., Sierra, B., Rodriguez, I. & Jauregi, E. (2019). Video Activity Recognition: State-of-the-Art. Sensors , 19(14), 3160.
Shan, J. & Akella, S. (2014, September). 3D human action segmentation and recognition using pose kinetic energy. In 2014 IEEE International Workshop on Advanced Robotics and Its Social Impacts, IEEE, pp. 69–75.
Sharma, S., Kiros, R. & Salakhutdinov, R. (2015). Action recognition using visual attention. arXiv preprint arXiv:1511.04119.
Sigurdsson, G.A., Gupta, A., Schmid, C., Farhadi, A. & Alahari, K. (2018). Charades-ego: a large-scale dataset of paired third and firstperson videos. arXiv preprint arXiv:1804 - 09626.
Singh, R., Sonawane, A. & Srivastava, R. (2019). Recent evolution of modern datasets for human activity recognition: a deep survey. Multimedia Systems. 26(2), 83–106.
Singh, S., Arora, C. & Jawahar, C.V. (2017). Trajectory aligned features for first person action recognition. Pattern Recognit, 62, 45–55.
Sonwalkar, P., Sakhare, T., Patil, A. & Kale, S. (2015). Hand gesture recognition for real time human machine interaction system. International Journal of Engineering Trends and Technology (IJETT), 19(5), 262{264.
Srijan, D., Michal, K., Francois, B. & Gianpiero, F. (2018). A Fusion of Appearance based CNNs and Temporal evolution of Skeleton with LSTM for Daily Living Action Recognition. arXiv 2018, arXiv:1802.00421v1.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D. & Rabinovich, A. (2015). In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9.
Torralba, A. & Efros, A. A. (2011, June). Unbiased look at dataset bias. In CVPR 2011, IEEE, pp. 1521–1528.
Wang, J., Liu, Z., Wu, Y. & Yuan, J. (2013). Learning Actionlet Ensemble for 3D Human Action Recognition. IEEE transactions on pattern analysis and machine intelligence, 36(5), 914–927.
Wang, P., Li, W., Ogunbona, P., Wan, J. & Escalera, S. (2018). RGB-Dbased human motion recognition with deep learning: A survey. In Computer Vision and Image Understanding, 171, pp. 118–139.
Xu, Z., Hu, J. & Deng, W. (2016, July). Recurrent convolutional neural network for video classification. In 2016 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE.
Zhang, L., Feng, Y., Han, J. & Zhen, X. (2016, March). Realistic human action recognition: When deep learning meets VLAD. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 1352–1356.
Zhang, N., Hu, Z., Lee, S. & Lee, E. (2017). Human Action Recognition Based on Global Silhouette and Local Optical Flow. Adv. Eng. Res., 134, 1–5.
Zhao, R., Ali, H. & Van der Smagt, P. (2017, September). Two-stream RNN/CNN for action recognition in 3D videos. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 4260–4267.
Abebe, G., Catala, A. & Cavallaro, A. (2018, August). A first-person vision dataset of office activities. In IAPR Workshop on Multimodal Pattern Recognition of Social Signals in Human Computer Interaction, Springer, Cham, pp. 27–37.
Alfaro, A., Mery, D. & Soto, A. (2016). Action recognition in video using sparse coding and relative features. arXiv preprint arXiv:1605.03222. DOI: 10.3390/app7010110.
Ali, K.H. & Wang, T. (2014, July). Learning features for action recognition and identity with deep belief networks. In 2014 International Conference on Audio, Language and Image Processing, IEEE, pp. 129{132 (2014).
Damen, D., Doughty, H., Farinella, G. M., Fidler, S., Furnari, A., Kazakos, E. & Wray, M. (2018). Scaling egocentric vision: The epic-kitchens dataset. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 720–736.
Fathi, A., Li, Y. & Rehg, J.M. (2012, October). Learning to recognize daily actions using gaze. In European Conference on Computer Vision (pp. 314–327). Springer, Berlin, Heidelberg.
Gu, C., Sun, C., Ross, D. A., Vondrick, C., Pantofaru, C., Li, Y. … Malik, J. (2018). AVA: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. doi:10.1109/cvpr.2018.00633)
Iwashita, Y., Takamine, A., Kurazume, R. & Ryoo, M.S. (2014, August). Firstperson animal activity recognition from egocentric videos. In 2014 22nd International Conference on Pattern Recognition (pp. 4310–4315). IEEE.
Li, Y., Liu, M. & Rehg, J. M. (2018). In the eye of beholder: joint learning of gaze and actions in first person video. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 619–635.
Li, Y., Ye, Z. & Rehg, J. M. (2015). Delving into egocentric actions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 287–295).
Pirsiavash, H. & Ramanan, D. (2012, June). Detecting activities of daily livingin first-person camera views. In 2012 IEEE conference on computer vision and pattern recognition, pp. 2847–2854. IEEE.
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overtting. Journal of Machine Learning Research 15(1), 1929–1958.
Steinkraus, D., Buck, I. & Simard, P. Y. (2005, August). Using GPUs for machine learning algorithms. In Eighth International Conference on Document Analysis and Recognition (ICDAR’05), IEEE, pp. 1115-1120.
Sung, J., Ponce, C., Selman, B. & Saxena, A. (2011). Human activity detection from rgbd images. CoRRabs/1107.0169 DOI:10.1109/APSIPA.2014.7041588
Albu, V. (2016). Measuring Customer Behavior with Deep Convolutional Neural Networks; BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 7(1), pp. 74–79.
Baldominos, A., Saez, Y. & Isasi, P. (2018). Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments. Sensors, 18(4), 1288.
Ballas, N., Yao, L., Pal, C. & Courville, A. (2015) . Delving Deeper into Convolutional Networks for Learning Video Reresentations. arXiv preprint arXiv:1511.06432.
Bojanowski, P., Lajugie, R., Bach, F., Laptev, I., Ponce, J., Schmid, C. & Sivic, J. (2014, September). Weakly supervised action labeling in videos under ordering constraints. In European Conference on Computer Vision, Springer, Cham, pp. 628–643.
Cao, L., Liu, Z. & Huang, T.S. (2010, June). Cross-dataset action detection. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp. 1998–2005.
Cippitelli, E., Gasparrini, S., Gambi, E. & Spinsante, S. (2016). A Human Activity Recognition System Using Skeleton Data from RGBD Sensors. Computational Intelligence and Neuroscience.
Cruz-Silva, J.E., Montiel-Pérez, J.Y. & Sossa-Azuela, H. (2019, October). 3-D Human Body Posture Reconstruction by Computer Vision. In Mexican International Conference on Artificial Intelligence, Springer, Cham, pp. 579–588.
Das, S., Koperski, M., Bremond, F. & Francesca, G. (2018). A fusion of appearance based CNNs and temporal evolution of skeleton with LSTM for daily live in action. Recognition ArXiv e-prints.
Delaitre, V., Laptev, I. & Sivic, J. (2010, August). Recognizing human actions in still images: a study of bag-of-features and part-based representations. In BMVC 2010-21st British Machine Vision Conference.
Faria, D.R., Premebida, C. & Nunes, U. (2014, August). A probabilistic approach for human everyday activities recognition using body motion from RGB-D images. In The 23rd IEEE International Symposium on Robot and Human Interactive Communication, IEEE, pp. 732–737.
Feichtenhofer, C., Pinz, A. & Zisserman, A. (2016). Convolutional twostream network fusion for video action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1933–1941.
Gaglio, S., Re, G.L. & Morana, M. (2014). Human Activity Recognition Process Using 3-D Posture Data. IEEE Transactions on Human-Machine Systems 45(5), 586–597.
Gao, Y., Xiang, X., Xiong, N., Huang, B., Lee, H.J., Alrifai, R., Jiang, X. & Fang, Z. (2018). Human Action Monitoring for Healthcare based on Deep Learning. IEEE Access, 6, 52277–52285.
Herath, S., Harandi, M. … Porikli, F. (2017). Going deeper into action recognition: A survey. Image and vision computing, 60, 4–21.
Idrees, H., Zamir, A. R., Jiang, Y. G., Gorban, A., Laptev, I., Sukthankar, R. … Shah, M. (2017). The THUMOS challenge on action recognition for videos ‘in the wild’. Computer Vision and Image Understanding, 155, 1–23.
Ijjina, E. P. & Krishna Mohan, C. (2016). Hybrid deep neural network model for human action recognition. Applied Soft Computing, 46, 936–952.
Jaouedi, N., Perales, F. J., Buades, J. M., Boujnah, N. & Bouhlel, M. S. (2020). Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model. Sensors, 20(17), 4944.
Ji, Y., Xu, F., Yang, Y., Shen, F., Shen, H. T. & Zheng, WS. (2018, October). A Large-scale RGB-D Database for Arbitrary-view Human Action Recognition. In Proceedings of the 26th ACM Multimedia Conference on Multimedia, pp. 1510–1518).
Khaire, P., Kumar, P. & Imran, J. (2018). Combining CNN Streams of RGB-D and Skeletal Data for Human Activity Recognition. Pattern Recognition Letters, 115, 107–116.
Kim, H. & Kim, I. (2015). Human Activity Recognition as Time-Series Analysis. Mathematical Problems in Engineering.
Kliper-Gross, O., Hassner, T. & Wolf, L. (2011). The action similarity labeling challenge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3), 615–621.
Koppula, H.S., Gupta, R. & Saxena, A. (2013). Learning human activities and object affordances from RGB-D videos. The International Journal of Robotics Research, 32(8), 951–970.
Krishna, R., Hata, K., Ren, F., Fei-Fei, L. & Carlos Niebles, J. (2017). Densecaptioning events in videos. In Proceedings of the IEEE International Conference on Computer Vision, pp. 706–715.
Latah, M. (2017). Human action recognition using support vector machines and 3D convolutional neural networks. Int. J. Adv. Intell. Informatics, 3(1), 47.
Liu, C., Hu, Y., Li, Y., Song, S. & Liu, J. (2017). PKU-MMD: a large scale benchmark for continuous multi-modal human action understanding. arXiv:1703.07475
Liu, J., Luo, J. & Shah, M. (2009, June). Recognizing realistic actions from video? in the Wild. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition , IEEE, pp. 1996–2003.
Ma, S., Bargal, S. A., Zhang, J., Sigal, L. & Sclaroff, S. (2017). Do less and achieve more: Training CNNs for action recognition utilizing action images from the Web. Pattern Recognition, 68, 334–345.
Manzi, A., Dario, P. & Cavallo, F. (2017). A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data. Sensors, 17(5), 1100.
Minnen, D., Westeyn, T., Starner, T., Ward, J.A. & Lukowicz, P. (2006). Performance metrics and evaluation issues for continuous activity recognition. Performance Metrics for Intelligent Systems ,4 , pp. 141–148.
Murad, A. & Ryun, J. Y. (2017). Deep Recurrent Neural Networks for Human Activity Recognition. Sensors, 17(11), 2556.
Ni, B., Pei, Y., Moulin, P. & Yan, S. (2013). Multilevel Depth and Image Fusion for Human Activity Detection. IEEE Transactions on Cybernetics, 43(5), 1383–1394.
Ohnishi, K., Kanehira, A., Kanezaki, A. & Harada, T. (2016). Recognizing activities of daily living with a wrist-mounted camera. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3103–3111.
Pham, H. H., Khoudour, L., Crouzil, A., Zegers, P. & Velastin Carroza, S. A. (2015). Video-based human action recognition using deep learning: a review.
Qin, Z., Zhang, Y., Meng, S., Qin, Z. & Choo, K. R. (2020). Imaging and fusing time series for wearable sensors based human activity recognition. Information Fusion, 53, 80–87.
Reddy, K. K. & Shah, M. (2013). Recognizing 50 human action categories of web videos. Machine Vision and Applications, 24(5), 971–981.
Rodríguez-Moreno, I., Martínez-Otzeta, J. M., Sierra, B., Rodriguez, I. & Jauregi, E. (2019). Video Activity Recognition: State-of-the-Art. Sensors , 19(14), 3160.
Shan, J. & Akella, S. (2014, September). 3D human action segmentation and recognition using pose kinetic energy. In 2014 IEEE International Workshop on Advanced Robotics and Its Social Impacts, IEEE, pp. 69–75.
Sharma, S., Kiros, R. & Salakhutdinov, R. (2015). Action recognition using visual attention. arXiv preprint arXiv:1511.04119.
Sigurdsson, G.A., Gupta, A., Schmid, C., Farhadi, A. & Alahari, K. (2018). Charades-ego: a large-scale dataset of paired third and firstperson videos. arXiv preprint arXiv:1804 - 09626.
Singh, R., Sonawane, A. & Srivastava, R. (2019). Recent evolution of modern datasets for human activity recognition: a deep survey. Multimedia Systems. 26(2), 83–106.
Singh, S., Arora, C. & Jawahar, C.V. (2017). Trajectory aligned features for first person action recognition. Pattern Recognit, 62, 45–55.
Sonwalkar, P., Sakhare, T., Patil, A. & Kale, S. (2015). Hand gesture recognition for real time human machine interaction system. International Journal of Engineering Trends and Technology (IJETT), 19(5), 262{264.
Srijan, D., Michal, K., Francois, B. & Gianpiero, F. (2018). A Fusion of Appearance based CNNs and Temporal evolution of Skeleton with LSTM for Daily Living Action Recognition. arXiv 2018, arXiv:1802.00421v1.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D. & Rabinovich, A. (2015). In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9.
Torralba, A. & Efros, A. A. (2011, June). Unbiased look at dataset bias. In CVPR 2011, IEEE, pp. 1521–1528.
Wang, J., Liu, Z., Wu, Y. & Yuan, J. (2013). Learning Actionlet Ensemble for 3D Human Action Recognition. IEEE transactions on pattern analysis and machine intelligence, 36(5), 914–927.
Wang, P., Li, W., Ogunbona, P., Wan, J. & Escalera, S. (2018). RGB-Dbased human motion recognition with deep learning: A survey. In Computer Vision and Image Understanding, 171, pp. 118–139.
Xu, Z., Hu, J. & Deng, W. (2016, July). Recurrent convolutional neural network for video classification. In 2016 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE.
Zhang, L., Feng, Y., Han, J. & Zhen, X. (2016, March). Realistic human action recognition: When deep learning meets VLAD. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 1352–1356.
Zhang, N., Hu, Z., Lee, S. & Lee, E. (2017). Human Action Recognition Based on Global Silhouette and Local Optical Flow. Adv. Eng. Res., 134, 1–5.
Zhao, R., Ali, H. & Van der Smagt, P. (2017, September). Two-stream RNN/CNN for action recognition in 3D videos. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 4260–4267.
Abebe, G., Catala, A. & Cavallaro, A. (2018, August). A first-person vision dataset of office activities. In IAPR Workshop on Multimodal Pattern Recognition of Social Signals in Human Computer Interaction, Springer, Cham, pp. 27–37.
Alfaro, A., Mery, D. & Soto, A. (2016). Action recognition in video using sparse coding and relative features. arXiv preprint arXiv:1605.03222. DOI: 10.3390/app7010110.
Ali, K.H. & Wang, T. (2014, July). Learning features for action recognition and identity with deep belief networks. In 2014 International Conference on Audio, Language and Image Processing, IEEE, pp. 129{132 (2014).
Damen, D., Doughty, H., Farinella, G. M., Fidler, S., Furnari, A., Kazakos, E. & Wray, M. (2018). Scaling egocentric vision: The epic-kitchens dataset. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 720–736.
Fathi, A., Li, Y. & Rehg, J.M. (2012, October). Learning to recognize daily actions using gaze. In European Conference on Computer Vision (pp. 314–327). Springer, Berlin, Heidelberg.
Gu, C., Sun, C., Ross, D. A., Vondrick, C., Pantofaru, C., Li, Y. … Malik, J. (2018). AVA: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. doi:10.1109/cvpr.2018.00633)
Iwashita, Y., Takamine, A., Kurazume, R. & Ryoo, M.S. (2014, August). Firstperson animal activity recognition from egocentric videos. In 2014 22nd International Conference on Pattern Recognition (pp. 4310–4315). IEEE.
Li, Y., Liu, M. & Rehg, J. M. (2018). In the eye of beholder: joint learning of gaze and actions in first person video. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 619–635.
Li, Y., Ye, Z. & Rehg, J. M. (2015). Delving into egocentric actions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 287–295).
Pirsiavash, H. & Ramanan, D. (2012, June). Detecting activities of daily livingin first-person camera views. In 2012 IEEE conference on computer vision and pattern recognition, pp. 2847–2854. IEEE.
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overtting. Journal of Machine Learning Research 15(1), 1929–1958.
Steinkraus, D., Buck, I. & Simard, P. Y. (2005, August). Using GPUs for machine learning algorithms. In Eighth International Conference on Document Analysis and Recognition (ICDAR’05), IEEE, pp. 1115-1120.
Sung, J., Ponce, C., Selman, B. & Saxena, A. (2011). Human activity detection from rgbd images. CoRRabs/1107.0169 DOI:10.1109/APSIPA.2014.7041588
Muhamada, A. W., & Mohammed, A. A. (2022). Review on recent Computer Vision Methods for Human Action Recognition. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(4), 361–379. https://doi.org/10.14201/ADCAIJ2021104361379
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