The motion analysis of the human body is an important topic of research in computer vision devoted to detecting, tracking, and understanding people's physical behavior. This strong interest is driven by a wide spectrum of applications in various areas such as smart video surveillance. Most research in behavior (or gesture) representation focusses on view-dependent representation, and some research on view invariance considers only information from 3-D models, which is effective under considerable changes of viewpoint. This paper introduces a view-independent behavior-analysis framework based on decision fusion in which distance and view angle factors are analyzed. This is a first effort to tackle the problem of behaviors under significant changes in view angle, and a first corresponding video database is built.