Data modeling and query processing for distributed surveillance systems

This paper presents data modeling and query processing for distributed surveillance systems. We define a metadata rule to search and manage information for distributed or heterogeneous surveillance systems. For human activity recognition, we propose a method that classifies these actions separately from complicated activities as a sequence of basic activities. In addition, we define the domain and range of relations based on the relationship between elements. Furthermore, we describe the state descriptors to represent an image sequence. To address the interaction of multiple objects, we classify human actions into symmetric or asymmetric actions. The prior motion model and the inference approach are applied adaptively according to environments. We define the grammar for the representation of the surveillance video and specify different query criteria for surveillance video retrieval. In the experiments, we show the prototype system that provides event detection, object identification, object tracking, face recognition, and activity recognition.