Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Evaluation of Multiple Object Tracking in Surveillance Video
Linköping University, Department of Electrical Engineering, Computer Vision.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Multiple object tracking is the process of assigning unique and consistent identities to objects throughout a video sequence. A popular approach to multiple object tracking, and object tracking in general, is to use a method called tracking-by-detection. Tracking-by-detection is a two-stage procedure: an object detection algorithm first detects objects in a frame, these objects are then associated with already tracked objects by a tracking algorithm. One of the main concerns of this thesis is to investigate how different object detection algorithms perform on surveillance video supplied by National Forensic Centre. The thesis then goes on to explore how the stand-alone alone performance of the object detection algorithm correlates with overall performance of a tracking-by-detection system. Finally, the thesis investigates how the use of visual descriptors in the tracking stage of a tracking-by-detection system effects performance. 

Results presented in this thesis suggest that the capacity of the object detection algorithm is highly indicative of the overall performance of the tracking-by-detection system. Further, this thesis also shows how the use of visual descriptors in the tracking stage can reduce the number of identity switches and thereby increase performance of the whole system.

Place, publisher, year, edition, pages
2019. , p. 50
Keywords [en]
Multiple Object Tracking, Tracking-by-Detection, Object Detection, Object Tracking, Deep Learning
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-157666ISRN: LiTH-ISY-EX--19/5245--SEOAI: oai:DiVA.org:liu-157666DiVA, id: diva2:1326842
External cooperation
Nationellt forensiskt centrum
Subject / course
Computer Vision Laboratory
Presentation
2019-06-05, Visionen, Linköping, 10:15 (English)
Supervisors
Examiners
Available from: 2019-06-19 Created: 2019-06-18 Last updated: 2019-06-19Bibliographically approved

Open Access in DiVA

fulltext(16866 kB)66 downloads
File information
File name FULLTEXT01.pdfFile size 16866 kBChecksum SHA-512
b2fec7d70dc722d1d8561a475338e43d0f8f58e966b9d07828f9285cba7fc87d54c2e77153bad8421780d696ea37590230a32b840574069778fbde31bbfd6352
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Nyström, Axel
By organisation
Computer Vision
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 66 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 377 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf