Human action recognition using rule based fuzzy motion feature templates

Abstract

This paper proposes a technique for human activity recognition in a video stream. To achieve high accuracy in activity recognition results, the method in its initial step deploys temporal template matching to recognize activities. As temporal templates are susceptible to get affected by speed, style and performance pattern of activity, so it becomes difficult to accurately differentiate among closely similar activities (e.g walking, running and jogging). The confusion in recognizing activities is reconciled by subsequent rule based activity distinction. The proposed method recognizes the human activities in video on various bench-marked data sets including KTH Dataset and Weizmann Dataset. Experimental results demonstrate the novelty of method with a wide spectrum of varied conditions. The average accuracy of the method is 97.20% under standard conditions.