Modeling and querying uncertain data for activity recognition systems using PostgreSQL
Activity Recognition (AR) systems interpret events in video streams by identifying actions and objects and combining these descriptors into events. Relational databases can be used to model AR systems by describing the entities and relationships between entities. This thesis presents a relational data model for storing the actions and objects extracted from video streams. Since AR is a sequential labeling task, where a system labels images from video streams, errors will be produced because the interpretation process is not always temporally consistent with the world. This thesis proposes a ...
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