Orientation Invariant Skeleton Feature (OISF): a new feature for Human Activity Recognition

Abstract

Human Activity Recognition is the process of identifying the activity of a person by analyzing continuous frames of a video. In many application areas, human activity identification is either a direct goal or it is a key segment of a bigger objective. Some of the examples are surveillance system, elder healthcare monitoring system, abnormal activity detection systems such as fight detection, theft detection etc. Robust and accurate activity recognition is a challenging task due to diverse reasons, such as changing ambient illumination, noise, background turbulence, camera placements etc. Existing literatures discuss some techniques for identifying human activity but these approaches are restricted to the case of videos recorded from static camera. The aim of the proposed approach is to fill this gap. In this proposed method, a new skeleton based feature for human activity recognition- “Orientation Invariant Skeleton Feature (OISF)”- is introduced and used to train Random Forest (RF) classifier for Human Activity Recognition. Efficiency of newly introduced feature OISF is analyzed for the videos recorded with multiple cameras positioned at two different slant angles. Experimental results reveal that the newly introduced feature OISF has minimal dependency on variations of camera orientation. Accuracy achieved is ≈ 99.30% with ViHASi dataset, ≈ 96.85% with KTH dataset and ≈ 98.34% with in-house dataset which is higher than those achieved by other researches with existing features. The improved result of human activity recognition in terms of accuracy proves the appropriateness of the proposed research in being used commercially.