The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
Open accessAbstract
Recurrent neural network (RNN) and long short-term memory (LSTM) have achieved great success in processing sequential multimedia data and yielded the state-of-the-art results in speech recognition, digital signal processing, video processing, and text data analysis. In this paper, we propose a novel action recognition method by processing the video data using convolutional neural network (CNN) and deep bidirectional LSTM (DB-LSTM) network. First, deep features are extracted from every sixth frame of the videos, which helps reduce the redundancy and complexity. Next, the sequential information among frame features is learnt using DB-LSTM network, where multiple layers are stacked together in both forward pass and backward pass of DB-LSTM to increase its depth. The proposed method is capable of learning long term sequences and can process lengthy videos by analyzing features for a certain time interval. Experimental results show significant improvements in action recognition using the proposed method on three benchmark data sets including UCF-101, YouTube 11 Actions, and HMDB51 compared with the state-of-the-art action recognition methods.
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Action Recognition with Improved Trajectories
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A survey of the recent architectures of deep convolutional neural networks
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09825-6 · Cited by 3,137
CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope
Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., & Ghayvat, H. (2021) · Electronics · doi:10.3390/electronics10202470 · Cited by 890
A Survey of Optimization Methods From a Machine Learning Perspective
Sun, S., Cao, Z., Zhu, H., & Zhao, J. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2950779 · Cited by 789
Convolutional Neural Networks Based Fire Detection in Surveillance Videos
Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., & Baik, S. W. (2018) · IEEE Access · doi:10.1109/access.2018.2812835 · Cited by 556
Self-citationIntelligent video surveillance: A review through deep learning techniques for crowd analysis
Sreenu, G., & Saleem Durai, M. A. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0212-5 · Cited by 460
Metadata
- Authors
- Ullah, A., Ahmad, J., Muhammad, K., Sajjad, M., & Baik, S. W.
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 1155-1166
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2769581371
- Cited by
- 756
- References
- 8
- FWCI
- 26.17
- Type
- journal article
- Language
- en
- Open access
- gold
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