Random forest based robust human activity detection system using hybrid feature

Abstract

Human Activity Recognition is analyzing surveillance videos of a person’s activity. Tracking and identifying activities is essential in various applications like fight detection, mob lynching, etc. Human activity recognition may be the primary or secondary goal of a significant problem target. Surveillance for fight detection, crowd violence, public attacks, mob lynching, public robbery, etc, are few to list. The researchers are getting done in this direction, and algorithms are being proposed for automatic activity recognition. These are typically restricted to the recordings made by stationary cameras, though. For automated Human Activity Recognition, a novel skeleton-based feature called ‘Orientation Invariant Skeleton Feature (OISF)’ was introduced in the earlier work. This work uses a hybrid feature, which is a combination of ‘OISF’ features (proposed by Neelam Dwivedi et al) and ‘FV1’ features (proposed by S Kumar et al) for human activity detection systems. The hybrid features used in this paper have a low dependence on changes in camera orientation, according to experimental results. The accuracy obtained is higher than that of earlier studies using existing features and is roughly 99.50% with the ViHASi dataset and 97.25% with the KTH dataset. This demonstrates that the proposed study is suitable for commercial use.