Identifying Human Actions Via Long-Term Recurrent Convolutional Network

Author(s)	K R Vignesh, J D Gowthm, Dr. K S Sivle
Country	India
Abstract	Automatic identification of human actions from videos has seen significant advancements. Typically, Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are employed individually for this purpose. CNNs are trained on pre-existing models to extract visual features from video frames. These features are then utilized by LSTMs to predict outcomes. However, integrating CNN and LSTM layers into a unified architecture known as Long Short-Term Recurrent Convolutional Network (LRCN) yields superior performance. Our study illustrates that a unified LRCN model achieves higher accuracy compared to using CNN and LSTM models separately
Keywords	Machine learning, LSTM, LRCN, CNN, Human Action Identification.
Field	Engineering
Published In	Volume 4, Issue 3, May-June 2023
Published On	2023-06-15

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