Recognizing Human Activity Using Hybrid Models of CNN and LSTM in Deep Learning (International Journal of Food and Nutritional Sciences, 2022)
Human Activity Recognition (HAR) is a time series categorization challenge that requiresdata from a number of timesteps in order to correctly classify the activities that are carriedout. In recent times, the usage of image datasets for activity recognition has increased,however good classification cannot be done with just one frame. To increase recognitionaccuracy, multiple frames of data and the context of environment are required. It is knownthat a video is made up of a number of still images (frames) that are quickly updated tocreate the illusion of motion. The hybrid models of Deep Learning (DL) algorithms likeConvolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) are proposedfor recognising the human activity from video dataset. The hybrid models, ConvolutionalLong Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Network(LRCN) are introduced to improve the accuracy of HAR on video dataset. The models will beevaluated on standard video datasets, and the outcomes will show how HAR has thepotential to significantly influence a number of industries. It has many applications in thefields of security, sports, and healthcare.