Modeling and querying uncertain data for activity recognition systems using PostgreSQL
Date
2012
Authors
Burnett, Kevin, author
Draper, Bruce, advisor
Ray, Indrakshi, advisor
Vijayasarathy, Leo, committee member
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Abstract
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 PostgreSQL function that uses the Viterbi algorithm to temporally smooth labels over sequences of images and to identify track windows, or sequential images that share the same actions and objects. The experiment design tests the effects that the number of sequential images, label count, and data size has on execution time for identifying track windows. The results from these experiments show that label count is the dominant factor in the execution time.
Description
Rights Access
Subject
PostgreSQL
Viterbi