A Spatio-Temporal Database Model on Transportation Surveillance Videos.
Xin Chen, Chengcui Zhang · 2006
With the rapid growth of multimedia data, there is an increasing need for robust multimedia database model. Such a model should be able to index spatio-temporal data thus efficient access to data whose geometry changes over time can be provided. In this paper, a spatio-temporal multimedia database model for managing transportation surveillance video data is proposed. The objective is to build a spatio-temporal database schema for transportation surveillance videos, in which queries can be answered easily and efficiently. The proposed spatio-temporal model for transportation surveillance videos combines the strength of two general-purpose spatio-temporal multimedia database models the Multimedia Augmented Transition Network model (MATN) and the Common Appearance Interval (CAI) model. While MATN model is good at modeling the replay of the multimedia presentation and the spatial-temporal relations of semantic objects in the video, it is not efficient in modeling or querying the trajectories of moving objects. In the mean time, while Common Appearance Interval (CAI) model can be used to better answer trajectorybased queries, it explicitly stores the spatial relations of pairs of objects in the model, which is considered redundant in transportation video databases. The proposed model bases its structure on MATNs and adopts the concept of CAI to segment transportation surveillance videos. Since this model is motivated by transportation surveillance applications, it has some domain specific features. It models each traffic light phase in a MATN-like network and models the corresponding video segment using CAIs. In addition, CAIs are further divided into subintervals and modeled by the sub-network structure in MATNs. In this paper, the proposed model, together with a brief introduction of the vehicle extraction/tracking/classification, is presented with its formal definition and some sample queries. The advantages of our model in comparison with other models are also demonstrated.