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- W4211154503 abstract "Human action recognition is one of the trending research topics in the field of computer vision. Human-computer interaction and video monitoring are broad applications that aid in the understanding of human action in a video. The problem with action recognition algorithms such as 3D CNN, Two-stream network, and CNN-LSTM is that they have highly complex models including a lot of parameters resulting in difficulty while training them. Such models require high configuration machines for real-time human action recognition. Therefore, present research proposes the use of 2D skeleton features along with a KNN classifier based HAR system to overcome the aforementioned problems of complexity and response time." @default.
- W4211154503 created "2022-02-13" @default.
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- W4211154503 date "2022-01-01" @default.
- W4211154503 modified "2023-09-27" @default.
- W4211154503 title "Multiview Human Action Recognition System Based on OpenPose and KNN Classifier" @default.
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- W4211154503 doi "https://doi.org/10.1007/978-981-16-8129-5_136" @default.
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