Hierarchical Recognition of Human Activities Interacting with Objects

The paper presents a system that recognizes humans interacting with objects. We delineate a new framework that integrates object recognition, motion estimation, and semantic-level recognition for the reliable recognition of hierarchical human-object interactions. The framework is designed to integrate recognition decisions made by each component, and to probabilistically compensate for the failure of the components with the use of the decisions made by the other components. As a result, human-object interactions in an airport-like environment, such as ´a person carrying a baggage´, ´a person leaving his/her baggage´, or ´a person snatching another´s baggage´, are recognized. The experimental results show that not only the performance of the final activity recognition is superior to that of previous approaches, but also the accuracy of the object recognition and the motion estimation increases using feedback from the semantic layer. Several real examples illustrate the superior performance in recognition and semantic description of occurring events.