Human Action Recognition in Videos using TensorFlow based on Features

One of the most prominent research fields in computer vision and machine learning probably is human-action recognition, which basically involves the identification and interpretation of human behaviors from video data. Applications include, amongst others, surveillance, sports analytics, and human-computer interface, ending on health care. Common challenge for most of the standard approaches on HAR involves complex temporal and spatial dynamics in video sequences. We are going to learn how to propose a hybrid model, in which we are going to make a combination of LSTMs and Convolutional Neural Networks in TensorFlow for such challenges. The CNN component can efficiently extract a lot of spatial features from the single frames in videos, including edges, textures, and shapes. Afterward, these features are input into the LSTM component, which offers strong temporal modeling across frames. Therefore, the combined architecture can leverage the advantages of both CNNs and LSTMs in such a way as to ensure strong and accurate action recognition. First of all, some preprocesses will be undertaken on the video data in this implementation for obtaining frames, followed by normalization of the pixel value. Afterward, the CNN model will learn to extract spatial features from frames, while the LSTM model will learn about different temporal patterns of various activities. Subsequently, finetuning on a labeled video dataset enhances the unified model.