FIOU Tracker: An Improved Algorithm of IOU Tracker in Video with a Lot of Background Inferences

Zhihua Chen, Guhao Qiu, Han Zhang, Bin Sheng, Ping Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Multiple object tracking(MOT) is a fundamental problem in video analysis application. Associating unreliable detection in a complex environment is a challenging task. The accuracy of multiple object tracking algorithms is dependent on the accuracy of the first stage object detection algorithm. In this paper, we propose an improved algorithm of IOU Tracker–FIOU Tracker. Our proposal algorithm can overcome the shortcoming of IOU Tracker with a small amount of computing cost that heavily relies on the precision and recall of object detection accuracy. The algorithm we propose is based on the assumption that the motion of background inference is not obvious. We use the average light flux value of the track and the change rate of the light flux value of the center point of the adjacent object as the conditions to determine whether the trajectory is to be retained. The tracking accuracy is higher than the primary IOU Tracker and another well-known variant VIOU Tracker. Our proposal method can also significantly reduce the ID switch value and fragmentation value which are both important metrics in MOT task.

Original languageEnglish
Title of host publicationAdvances in Computer Graphics - 37th Computer Graphics International Conference, CGI 2020, Proceedings
EditorsNadia Magnenat-Thalmann, Constantine Stephanidis, George Papagiannakis, Enhua Wu, Daniel Thalmann, Bin Sheng, Jinman Kim, Marina Gavrilova
PublisherSpringer Science and Business Media Deutschland GmbH
Pages145-156
Number of pages12
ISBN (Print)9783030618636
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event37th Computer Graphics International Conference, CGI 2020 - Geneva, Switzerland
Duration: 20 Oct 202023 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12221 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference37th Computer Graphics International Conference, CGI 2020
Country/TerritorySwitzerland
CityGeneva
Period20/10/2023/10/20

Keywords

  • Drone video
  • FIOU Tracker
  • Multiple object tracking

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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