Learning, detection and representation of multi-agent events in videos

In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, and propose a novel approach for learning, detecting and representing events in videos. The proposed approach has three main steps. First, in order to learn the event structure from training videos, we automatically encode the sub-event dependency graph, which is the learnt event model that depicts the conditional dependency between sub-events. Second, we pose the problem of event detection in novel videos as clustering the maximally correlated sub-events using normalized cuts. The principal assumption made in this work is that the events are composed of a highly correlated chain of sub-events that have high weights (association) within the cluster and relatively low weights (disassociation) between the clusters. The event detection does not require prior knowledge of the number of agents in an event and does not make any assumptions about the length of an event. Third, we recognize the...

• Type: Journal
• Year: 2007
• Where: AI
• Authors: Asaad Hakeem, Mubarak Shah
• Keywords: AI 2007, Artificial Intelligence, Event Detection, Event Model, Learnt Event Model