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- W4387386418 abstract "The recognition of human activities in videos is a relevant area of study due to its real-life applications, such as surveillance, security, healthcare, human-machine interaction, and monitoring. This research compares two recognition approaches, called simple and hybrid, using two specific data sets. The first set includes three classes: yoga, exercise, and dance; the second is a sample from the Kinetics-700 set, with five activities, four of which are violent. Both sets present low variability between classes and high variability within classes. To reduce computational costs, a pre-trained CNN model and simple techniques for reducing computational resources are used. The hybrid approach uses an additional model with three variants: GRU, LSTM, or BiLSTM. Even though all the models presented similar results, the simple approach, using a pre-trained architecture and a reconstructed top-head, proved to be the most effective, reaching an accuracy of 94%, while the hybrid approach using LSTM layers obtained 90%. The model demonstrated an adequate classification of violent activities, which could serve as a basis for developing a surveillance and security systems." @default.
- W4387386418 created "2023-10-06" @default.
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- W4387386418 date "2023-01-01" @default.
- W4387386418 modified "2023-10-16" @default.
- W4387386418 title "Human Actions Recognition System Based on Neural Networks" @default.
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- W4387386418 doi "https://doi.org/10.1007/978-3-031-45438-7_4" @default.
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