Event prediction with rough-fuzzy sets

Abstract

This article proposes a new methodology of unsupervised event prediction from videos. Detecting events from videos without prior information is a challenging task, as there are no well-accepted definitions about events in a video. It is commonly known that the presence of moving elements in a video scene could be considered as part of an event. The possibility of an event occurring becomes higher if there is an abrupt change in the motion patterns of different object(s) present in that video scene. In this paper, we defined a method to model this phenomenon of object motion. Since we have not considered any prior information while modeling it, the initial event and nonevent classification is carried out with rough set-based approximations, namely positive, boundary, and negative, in the incomplete knowledge base, resulting in an event-nonevent rough sets. We generate three regions with rough sets. Negative class labels are assigned for static objects and those moving with predictable paths. The objects with a huge change in motion are labeled to be positive events. The remaining objects are kept in the boundary region. However, if there is a gradual change in the motion pattern, there arises some possibility of an event occurring. To define the terms, like possible events and must be event, we have fuzzified the boundary region of event-nonevent rough set and assigned different degrees of possibility of an event to occur if there is a change in motion patterns in the trajectory of the objects. That is, the event, nonevent regions are classified with rough sets, and the boundary region is fuzzified with fuzzy sets. We have validated this newly defined event-nonevent rough-fuzzy sets with experimental demonstrations where the proposed method successfully predicted the events to occur in video sequences.