Research on Action Recognition and Content Analysis in videos based on DNN and MLN
Open accessAbstract
In the current era of multimedia information, it is increasingly urgent to realize intelligent video action recognition and content analysis. In the past few years, video action recognition, as an important direction in computer vision, has attracted many researchers and made much progress. First, this paper reviews the latest video action recognition methods based on Deep Neural Network and Markov Logic Network. Second, we analyze the characteristics of each method and the performance from the experiment results. Then compare the emphases of these methods and discuss the application scenarios. Finally, we consider and prospect the development trend and direction of this field.
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Deep learning can be performed by various types of neural networks such as CNNs, DNNs, and RNNs, to recognize objects, track movements, and analyze behavior in video data.
“Deep learning can be performed by various types of neural networks such as CNNs [39][40][41][42], DNNs [43,44], and RNNs [45,46], to recognize objects, track movements, and analyze behavior in video data [47].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [43]
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A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
“Deep learning can be performed by various types of neural networks such as CNNs [39–42], DNNs [43,44], and RNNs [45,46], to recognize objects, track movements, and analyze behavior in video data [47].”
Background
- Authors
- Song, W., Yu, J., Zhao, X., & Wang, A.
- Affiliations
- Minzu University of China (CN)
- Beijing Jiaotong University (CN)
- Corresponding author
- Song, Wei
- Venue
- Computers, Materials & Continua
- Publisher
- Tech Science Press
- Published
- 2019
- Volume
- 61
- Issue
- 3
- Pages
- 1189-1204
- ISSN
- 1546-2218 (ISSN-L)
- 1546-2226
- OpenAlex
- W3129313461
- Cited by
- 10
- References
- 0
- Citations by year
- 2023: 1 · 2022: 2 · 2021: 6 · 2020: 1
- FWCI
- 1.01
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Computers, materials & continua/Computers, materials & continua (Print) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- video action recognition, video content analysis, deep neural network, Markov logic networks, intelligent video analysis, multimedia data
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