Abstract Recognition of complex activities from surveillance video requires detection and temporal ordering of its constituent “atomic” events. It also requires the capacity to robustly track individuals and maintain their identities across single as well as multiple camera views. Identity maintenance is a primary source of uncertainty for activity recognition and has been traditionally addressed via different appearance matching approaches. However these approaches, by themselves, are inadequate. In this paper, we propose a prioritized, multivalued, default logic based framework that allows reasoning about the identities of individuals. This is achieved by augmenting traditional appearance matching with contextual information about the environment and self identifying traits of certain actions. This framework also encodes qualitative confidence measures for the identity decisions it takes and finally, uses this information to reason about the occurrence of certain predefined activities in video.
Multivalued Default Logic for Identity Maintenance in Visual Surveillance
Computer Vision – ECCV 2006 ; 3 ; 119-132
Lecture Notes in Computer Science ; 3954 , 3
2006-01-01
14 pages
Article/Chapter (Book)
Electronic Resource
English
Multivalued Default Logic for Identity Maintenance in Visual Surveillance
British Library Conference Proceedings | 2006
|Representation of multivalued logic functions
NTRS | 1979
|Omni-directional visual surveillance
British Library Online Contents | 2004
|Space surveillance satellite catalog maintenance
AIAA | 1990
|Multivalued-Velocity-Field Model of Turbulence
NTRS | 1983
|