Human Arm-Leg Smart Gesture-Based Control in Human Computer Interaction Applications

This paper introduces a new model for arm and leg gestures recognition in a video stream. The proposed model recognizes a set of six specific hand gestures and two specific leg gestures, namely: Full Right-Hand Wave, Full Left-Hand Wave, Full left and right wave, Top Right-Hand Wave, Top Left-Hand Wave, Top both hand wave, Right leg, Left leg, Clapping. The proposed model consists of five phases: video acquisition and preprocessing, video segmentation or depth segmentation, detection, tracking, and classification. The proposed model overcomes the limitations found in previously proposed models as it can be applied on non-stationary background, can deal with noisy video input and requires less time consumption. The advantages mentioned above are achieved due to our most important contribution in the selection of the suitable algorithm that performs our goal efficiently in each phase of the proposed model. The proposed model integrates the separation of foreground movements in video segmentation phase with multi-layer Viola-Jones algorithm in detection phase. The output of these two phases is deployed in tracking motion of the moving region of interest using clustering of feature points, the output of which can be used for understanding simultaneously performed body hand-leg gestures. Our framework uses Kinect camera to connect video streams and integrates various techniques to make tracking tasks efficient. Experiments have been carried out to demonstrate the effectiveness of the proposed model on different benchmarked datasets and a newly generated dataset that was made specifically for this proposed model. IXMAS, Weizmann, and G3d Depth dataset have been used to validate the proposed model which demonstrated an outstanding improvement regarding accuracy for the iXMAS dataset and the G3d dataset.