University of Houston

Human activity recognition is one of the most challenging problems that have received considerable attention from the computer vision community in recent years. Its applications are diverse, spanning from its use in activity understanding for intelligent surveillance systems to improving human-computer interactions. The goal of human activity recognition is to automatically recognize ongoing activities from an unknown video (i.e. a sequence of image frames). The challenges in solving this problem are multifold due to the complexity of human motions, the spatial and temporal variations exhibited due to differences in duration of different activities performed, the changing spatial characteristics of the human form and the contextual information in performing each activity. A number of approaches have been proposed to address these challenges over the past few years by trying to design effective, compact descriptors for human activity encoding act ivity characteristics with context, but the mechanisms for incorporating them are not unique. In this work, we explore more complex descriptors and methods for human activity recognition encoding contextual information and its application to both individual activities and group activities.