An object-based algorithm for surveillance video compression

The amount of video data generated by the security surveillance cameras is stupendous as cameras track peoples activity in a number of places like university campus, shopping malls, office place etc. There is a huge need to archive these videos, organize them effectively for security purpose and als...

Full description

Saved in:
Bibliographic Details
Main Author: Divya, Venkatraman
Other Authors: Anamitra Makur
Format: Theses and Dissertations
Language:English
Published: 2009
Subjects:
Online Access:http://hdl.handle.net/10356/18770
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-18770
record_format dspace
spelling sg-ntu-dr.10356-187702023-07-04T15:22:38Z An object-based algorithm for surveillance video compression Divya, Venkatraman Anamitra Makur School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing The amount of video data generated by the security surveillance cameras is stupendous as cameras track peoples activity in a number of places like university campus, shopping malls, office place etc. There is a huge need to archive these videos, organize them effectively for security purpose and also for further analysis and research purpose. Because of the volume of data, video data needs to be compressed to reduce storage space required by them. This project is a step in the direction of compression of surveillance videos. The initial part of the project was aimed at obtaining a good segmentation of objects (people) in the security video. Perfect segmentation of moving people in the video was a challenge because of moving cast shadows and hence the project was directed towards eliminating shadows in objects during segmentation Different techniques were studied and an optimum object segmentation using adaptive threshold was proposed, implemented and tested. Better segmentation led to more accurate object-based motion vector estimation. The second part of the work involved the coding of the residual error object which is obtained by subtracting the original frame and the motion compensated frame. The theory of compressive sensing was studied and was used to code the error object, because of sparse representation of the error object. Different techniques of implementation of compressive sensing for error coding are discussed and compared Compressive sensing based coding was found comparable to the usual shape adaptive transform coding techniques. This report consolidates the steps involved in each stage in implementation of the compression of surveillance video and with comparative studies between different techniques. Master of Science (Signal Processing) 2009-07-17T07:47:51Z 2009-07-17T07:47:51Z 2008 2008 Thesis http://hdl.handle.net/10356/18770 en 63 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Divya, Venkatraman
An object-based algorithm for surveillance video compression
description The amount of video data generated by the security surveillance cameras is stupendous as cameras track peoples activity in a number of places like university campus, shopping malls, office place etc. There is a huge need to archive these videos, organize them effectively for security purpose and also for further analysis and research purpose. Because of the volume of data, video data needs to be compressed to reduce storage space required by them. This project is a step in the direction of compression of surveillance videos. The initial part of the project was aimed at obtaining a good segmentation of objects (people) in the security video. Perfect segmentation of moving people in the video was a challenge because of moving cast shadows and hence the project was directed towards eliminating shadows in objects during segmentation Different techniques were studied and an optimum object segmentation using adaptive threshold was proposed, implemented and tested. Better segmentation led to more accurate object-based motion vector estimation. The second part of the work involved the coding of the residual error object which is obtained by subtracting the original frame and the motion compensated frame. The theory of compressive sensing was studied and was used to code the error object, because of sparse representation of the error object. Different techniques of implementation of compressive sensing for error coding are discussed and compared Compressive sensing based coding was found comparable to the usual shape adaptive transform coding techniques. This report consolidates the steps involved in each stage in implementation of the compression of surveillance video and with comparative studies between different techniques.
author2 Anamitra Makur
author_facet Anamitra Makur
Divya, Venkatraman
format Theses and Dissertations
author Divya, Venkatraman
author_sort Divya, Venkatraman
title An object-based algorithm for surveillance video compression
title_short An object-based algorithm for surveillance video compression
title_full An object-based algorithm for surveillance video compression
title_fullStr An object-based algorithm for surveillance video compression
title_full_unstemmed An object-based algorithm for surveillance video compression
title_sort object-based algorithm for surveillance video compression
publishDate 2009
url http://hdl.handle.net/10356/18770
_version_ 1772828177951883264