Abstract
Human activity recognition aims at recognising and interpreting the activities of humans automatically from videos. Among the activities of humans, identifying the interactions between human within minimal computation time and reduced misclassification rate is a cumbersome task. Hence, an interaction-based human activity recognition system is proposed in this paper that utilises silhouette features to identify and classify the interactions between humans. The main issues that affect the performance of activity recognition are sudden illumination changes, detection of static human, data discrimination, data variance, crowding problem, and computational complexity. To accomplish the preceding issues, three new algorithms named weight-based updating Gaussian mixture model (wu-GMM), spatial dissemination-based contour silhouettes (SDCS), and weighted constrained dynamic time warping (WCDTW) are proposed. Experiments are conducted with the gaming dataset and Kinect interaction dataset to show that the proposed system recognises the interactions with reduced misclassification rate and minimal processing time compared to the existing system.
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