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 "walk," "run," "jump," "side," "bend," "wave1," "wave2," "pjump," "jack," and "skip"), was used in the trials. A variety of the tree-based classifiers, including Random-Tree, the Random-Forest, and the Decision-Tree (J48), were also used. According to the experimental findings, this method uses the Weizmann dataset to recognize the activities with an overall quality rate of 94.20%. This is more effective than other tree-based algorithms. Performance of the suggested strategy is on par with that of well-established, established procedures.