University of Houston

Human activity recognition is one of the most challenging problems that has 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 activity charact eristics with context, but the mechanisms for incorporating them are not unique. In this work, I explore complex descriptors that can encode contextual information leading to improved methods for human activity recognition for both single and group activities.