The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition
Abstract
Human activity recognition in videos with convolutional neural network (CNN) features has received increasing attention in multimedia understanding. Taking videos as a sequence of frames, a new record was recently set on several benchmark datasets by feeding frame-level CNN sequence features to long short-term memory (LSTM) model for video activity recognition. This recurrent model-based visual recognition pipeline is a natural choice for perceptual problems with time-varying visual input or sequential outputs. However, the above-mentioned pipeline takes frame-level CNN sequence features as input for LSTM, which may fail to capture the rich motion information from adjacent frames or maybe multiple clips. Furthermore, an activity is conducted by a subject or multiple subjects. It is important to consider attention that allows for salient features, instead of mapping an entire frame into a static representation. To tackle these issues, we propose a novel pipeline, saliency-aware three-dimensional (3-D) CNN with LSTM, for video action recognition by integrating LSTM with salient-aware deep 3-D CNN features on videos shots. Specifically, we first apply saliency-aware methods to generate saliency-aware videos. Then, we design an end-to-end pipeline by integrating 3-D CNN with LSTM, followed by a time series pooling layer and a softmax layer to predict the activities. Noticeably, we set a new record on two benchmark datasets, i.e., UCF101 with 13 320 videos and HMDB-51 with 6766 videos. Our method outperforms the state-of-the-art end-to-end methods of action recognition by 3.8% and 3.2%, respectively on above two datasets.
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Cites
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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
Artificial intelligence for the metaverse: A survey
Huynh-The, T., Pham, Q.-V., Pham, X.-Q., Nguyen, T. T., Han, Z., & Kim, D.-S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105581 · Cited by 710
Human Action Recognition From Various Data Modalities: A Review
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Liu, M., Li, F., Yan, H., Wang, K., Ma, Y., Shen, L., & Xu, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116459 · Cited by 568
Metadata
- Authors
- Wang, X., Gao, L., Song, J., & Shen, H.
- Venue
- IEEE Signal Processing Letters
- Published
- 2017-04
- Volume
- 24
- Issue
- 4
- Pages
- 510-514
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2522485430
- Cited by
- 265
- References
- 33
- FWCI
- 20.08
- Type
- journal article
- Language
- en
- Open access
- closed
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