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This paper used tensorflow and keras library to build a deep learning environment. Designed and established a deep 3D convolutional network model and Long Short-Term Memory network model, using UCF-101 dataset known category videos as training samples to train the network. Some videos in the dataset were used as test samples to verify the recognition performance of the network model and realize classification. Finally, Tensorboard was used to visually analyze the network training process. The experimental results show that the model has better video recognition performance.
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References
Zhu, F., Shao, L., Xie, J., et al.: From handcrafted to learned representations for human action recognition: a survey. Image Vis. Comput. 55, 42–52 (2016)
Najafi, A., Hasanlou, M., Akbari, V.: Land cover changes detection in polarimetric SAR data using algebra, similarity and distance based methods. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 42, 195–200 (2017)
Dhulekar, P., Gandhe, S.T., Chitte, H., et al.: Human action recognition: an overview. In: Satapathy, S., Bhateja, V., Joshi, A. (eds.) Proceedings of the International Conference on Data Engineering and Communication Technology, vol. 468, pp. 481–488. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-1675-2_48
Gupta, V., Singh, J.P.: Study and analysis of back-propagation approach in artificial neural network using HOG descriptor for real-time object classification. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds.) Soft Computing: Theories and Applications, vol. 742, pp. 45–52. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0589-4_5
Rashid, M., Khan, M.A., Sharif, M., et al.: Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features. Multimedia Tools Appl. 78(12), 15751–15777 (2019). https://doi.org/10.1007/s11042-018-7031-0
Liu, L., Hu, F., Zhao, J.: Action recognition based on features fusion and 3D convolutional neural networks. In:2016 9th International Symposium on Computational Intelligence and Design (ISCID), vol. 1, pp. 178–181. IEEE (2016)
Xu, Z., Vilaplana, V., Morros, J.R.: Action tube extraction based 3D-CNN for RGB-D action recognition. In: 2018 International Conference on Content-Based Multimedia Indexing (CBMI), pp. 1–6. IEEE (2018)
Li, C., Sun, S., Min, X., et al.: End-to-end learning of deep convolutional neural network for 3D human action recognition. In: 2017 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 609–612. IEEE (2017)
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Ren, J., Shi, H., Cao, J. (2022). Research and Practice of Video Recognition Based on Deep Learning. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 854. Springer, Singapore. https://doi.org/10.1007/978-981-16-9423-3_69
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DOI: https://doi.org/10.1007/978-981-16-9423-3_69
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