Real time human action recognition using triggered frame extraction and a typical CNN heuristic

Abstract Recognition of human actions with optimum accuracy and with less computational overhead has always been a challenging task. A suitable framework towards this problem can provide a robust solution for numerous domains of expertise that demands automatic surveillance. In this paper, a two-fold framework has been proposed for the frame extraction and recognition of human action therein from a video input. A Fuzzy inferencing technique has been suitably used for the purpose of video frame extraction in a smarter way. Frames are extracted from a video only when an event of action is initiated and about to commit. By this the additional computational load of continuous frame extraction is eased efficiently. Subsequent to this process, we also introduce a typical convolutional neural network (CNN) that is used for the purpose of human action recognition. This typical CNN has been designed so as to suit the nature of input that is supplied to it. Experimental evaluation has been performed on the standard HMDB51 database. A total of eight distinct and prominent actions which are mostly common in daily routine of human life are considered for the purpose. Performance comparison of the proposed scheme with other state of the art schemes is also performed with three different measures. Evaluation results for the proposed scheme indicates overall rate of accuracies of 96.5% and 98% for frame extraction and action recognition respectively. The proposed scheme outperformed the others in most of the measuring aspects.