Authors : Neetu Pal

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/37ru5tb3

Scribd : https://tinyurl.com/5yhuzv7j

DOI : https://doi.org/10.38124/ijisrt/26jun1195

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Recently, recognition of activities has gained popularity as a study topic in the ubiquitous subject of computer science. Recently, there has been a lot of interest in recognizing human actions in videos. Most PC vision functions, including human-computer interactions, suspicious activity detection, and medical activity detection, are predicated on the idea of activity detection. One of the best methods for working with activity data is to use deep learning technology and its various models. Activity detection is the process of automatically labelling activity from video frames. Deep networks have led to the separation of the activity detection process into two categories: the automated characteristic extraction approach and the classic characteristic-based approach. This research work, which is based on a conventional and automatic technique, focuses on several approaches employed in current literature. With the advent of repeating neural networks and convolutional neural networks, the entry modality's machine learning capability provides it the primary option to use for activity recognition. This paper examined several methods, considering their technique, datasets, accuracy, and classifiers. An outline of the HAR's importance, difficulties, and potential developments is provided in this study. Additionally, briefly discuss the HAR system using a variety of potential methods.

Keywords : Deep Learning, HAR, LSTM, ConvLSTM, LRCN, Convolutional.

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