Human Motion Detection Using Spatio-Temporal Volumes in Virtual Reality Environments

With the emerging of the new applications like virtual reality in image processing and machine vision, it is required to have more perfect motion detection ways. In this paper, we propose combined similarity criterion motion detector (CSCMD) method for Human motion detection in Virtual Reality Environments. In the proposed method, spatio-temporal volumes are constructed for human motions. Spatio-temporal volume unifies the analysis of spatial and temporal information by constructing a volume of data in which consecutive images are stacked to form a third, temporal dimension. These volumes are then analyzed by constructing residual error and calculating the criterion number to extract the motion pixels as anomalies and constant pixels as normal ones. Experimental results are illustrated using intuitive images, receiver-operating-characteristic (ROC) curves, area under curve (AUC) values and Recognition accuracy and compared with some popular and previous methods. In the ROC curves, thresholds are constructed by changing the appropriate coefficients based on the algorithm. Higher AUC values, detection rates, Recognition accuracy and lower false rates, prove the superiority and effectiveness of the proposed method for human motion detection and Human Computer Interaction in virtual reality environments.