Human Recognization Activity and Maximum Motion Representation in Surveillance Video

Recognizing human actions in the real-world environment finds vital applications, Hence, machine vision studies in this field become crucial. This research aims to extract human activity from video sequences. The number of activities are required for human action recognition, including the gathering of visual data, the identification and presentation of robust features, and the training of classifiers with strong discriminative abilities. The action recognition method employed in this research is based on maximal motion identification. The Region of Interest (ROI) difference picture is utilized to extract motion information, and the Block Based Motion Intensity Code (BBMIC) is extracted as a feature. The Weizmann action dataset, which includes 10 actions (including