3-D dataset for Human Activity Recognition in video surveillance

Abstract

We introduces a new 3-D video dataset to assess the performance of Human Activity Recognition system in indoor and outdoor environment. This dataset also help to check the performance of activity recognition algorithms against the effect of varying illumination, background and viewpoint. The available dataset for activity recognition are simple and most of them contain RGB information only as well as fewer complex and far away from real world general scenario. This dataset consist of RGB and depth information for all the activities performed under different illumination condition and viewpoint. This dataset includes ten activities performed by eleven subjects in four illumination condition and three viewpoints. Around one hundred and ten videos of one hour duration are recorded and annotated. We believe that the additional depth information provided will be useful for researcher in analyzing the performance of activity recognition algorithms for real time implementation. Large no of dataset are publicly available but most of them are less complex and consist of RGB data only. The proposed dataset is more complex and comprises of depth information along with RGB.