Recognising Human Actions Using Long-Term Recurrent Convolutional Network (LRCN)

The study of human activity recognition has gained popularity. It is employed to make sense of the actions taken by the people in the films. The CNN and LSTM models, which were trained independently, can be used to do this. Using a pre-trained model, we can use this CNN model to extract spatial characteristics from the frames that were taken from the movies. The action depicted in the videos can then be predicted using an LSTM model by using the features that CNN extracted. Alternatively, combining convolutional and LSTM layers into a single model using the Long Recurrent Convolutional Network is a more effective way to implement it. We discovered that using a single model was more accurate than using each model separately.