Graduation Semester and Year
2016
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Engineering
Department
Computer Science and Engineering
First Advisor
Sharma Chakravarthy
Abstract
Automating video stream processing for inferring situations of interest has been an ongoing challenge. This problem is currently exacerbated by the volume of surveillance/monitoring videos generated. Currently, manual or context-based customized techniques are used for this purpose. To the best to our knowledge the attempted work in this area use a custom query language to extract data and infer simple situations from the video streams, thus adding an additional overhead to learn their query language. Objective of the work in this thesis is to develop a framework that extracts data from video streams generating a data representation such that simple “what-if” kind of situations can be evaluated from the extracted data by posing queries in a Non-procedural manner (SQL or CQL without using any custom query languages). This thesis proposes ways to pre-process videos so as to extract the needed information from each frame. It elaborates on algorithms and experimental results for extracting objects, their features (location, bounding box, and feature vectors), and their identification across frames, along with converting all that information into an expressive data model. Pre-processing of video streams to extract queryable representation involves parameters and techniques that are context-based, that is, dependent on the type of video streams and the type of objects present in them. And lot of tuning of values or experiments is essential to choose right techniques or right values for these parameters. This thesis additionally, proposes starting values for such tuning or experiments, in order to reach the right values.
Keywords
Video stream processing, Computer vision, Stream processing, Continuous video querying
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Annappa, Manish Kumar, "Extracting and Modeling Useful Information from Videos for Supporting Continuous Queries" (2016). Computer Science and Engineering Theses. 451.
https://mavmatrix.uta.edu/cse_theses/451
Comments
Degree granted by The University of Texas at Arlington