Object detection and tracking

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

The availability of an increasing amount of computational power and large-scale public data sets has driven the field of object detection and tracking with an unprecedented development speed, finding applications in many areas. This chapter surveys the most prominent methods in the field. We first formulate the problems of object detection and single and multiple object tracking, and then present the most relevant methodologies which have successfully been developed to solve these problems. The chapter includes a comprehensive treatment of two-stage, one-stage and anchor-free object detection methods. Both single object and multiple object tracking methods are reviewed in the chapter. The former includes methods based on correlation filters and deep learning, including similarity learning, whereas the latter present online and offline multiple object tracking techniques. Online methods, including deep features driven and detection-based methods, rely on visual representations, while offline methods are mostly based on graph optimization.

Original languageEnglish
Title of host publicationDeep Learning for Robot Perception and Cognition
EditorsAlexandros Iosifidis, Anastasios Tefas
PublisherAcademic Press
Pages243-278
Number of pages36
ISBN (Electronic)9780323857871
ISBN (Print)9780323885720
DOIs
Publication statusPublished - 2022
Publication typeA3 Book chapter

Keywords

  • Multiple object tracking
  • Object detection
  • Object tracking
  • Single object tracking
  • Visual object tracking

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Computer Science(all)

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