Simple online and realtime tracking

Alex Bewley, Zongyuan Ge, Lionel Ott, Fabio Ramos, Ben Upcroft

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

320 Citations (Scopus)

Abstract

This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking performance, where changing the detector can improve tracking by up to 18.9%. Despite only using a rudimentary combination of familiar techniques such as the Kalman Filter and Hungarian algorithm for the tracking components, this approach achieves an accuracy comparable to state-of-the-art online trackers. Furthermore, due to the simplicity of our tracking method, the tracker updates at a rate of 260 Hz which is over 20x faster than other state-of-the-art trackers.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing (ICIP 2016)
EditorsFernando Pereira, Gaurav Sharma
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3464-3468
Number of pages5
ISBN (Electronic)9781467399616
ISBN (Print)9781467399623
DOIs
Publication statusPublished - 3 Aug 2016
Externally publishedYes
EventIEEE International Conference on Image Processing 2016 - Phoenix Convention Center, Phoenix, United States of America
Duration: 25 Sep 201628 Sep 2016
Conference number: 23rd
http://2016.ieeeicip.org/

Conference

ConferenceIEEE International Conference on Image Processing 2016
Abbreviated titleICIP 2016
CountryUnited States of America
CityPhoenix
Period25/09/1628/09/16
Internet address

Keywords

  • Computer Vision
  • Data Association
  • Detection
  • Multiple Object Tracking

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