A Structured Representation to the Group Behavior Recognition Issue

Abstract

Abstract. The behavior recognition is one of the most prolific lines of research in recent decades in the field of computer vision. Within this field, the majority of researches have focused on the recognition of the activities carried out by a single individual, however this paper deals with the problem of recognizing the behavior of a group of individuals, in which relations between the component elements are of great importance. For this purpose it is exposed a new representation that concentrates all necessary information concerning relations two to two present in the group, and the semantics of the different groups formed by individuals and training (or structure) of each one of them. The work is presented with the dataset created in CVBASE06 dealing the European handball

A Feature Selection Approach to the Group Behavior Recognition Issue Using Static Context Information

“…The behavior of each element in a group depends on the behavior of the other elements, and the combination of individual behaviors results in a high-level group behavior. This type of behavior is observed in many situations, like RoboCup [6], team sports [7–9], a military parade [10], automatic camera surveillance in public places [6, 11, 12], and so forth. Depending on the type of analyzed features, all these works could be classified into two main streams: logical reasoning and geometrical reasoning.…”