CDT: Cooperative detection and tracking for tracing multiple objects in video sequences

Han Ul Kim, Chang-Su Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

A cooperative detection and model-free tracking algorithm, referred to as CDT, for multiple object tracking is proposed in this work. The proposed CDT algorithm has three components: object detector, forward tracker, and backward tracker. First, the object detector detects targets with high confidence levels only to reduce spurious detection and achieve a high precision rate. Then, each detected target is traced by the forward tracker and then by the backward tracker to restore undetected states. In the tracking processes, the object detector cooperates with the trackers to handle appearing or disappearing targets and to refine inaccurate state estimates. With this detection guidance, the model-free tracking can trace multiple objects reliably and accurately. Experimental results show that the proposed CDT algorithm provides excellent performance on a recent benchmark. Furthermore, an online version of the proposed algorithm also excels in the benchmark.

Original languageEnglish
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
PublisherSpringer Verlag
Pages851-867
Number of pages17
Volume9910 LNCS
ISBN (Print)9783319464657
DOIs
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9910 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Keywords

  • Joint detection and tracking
  • Model-free tracking
  • Multiple object tracking
  • Object detection
  • Online multi-object tracking

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kim, H. U., & Kim, C-S. (2016). CDT: Cooperative detection and tracking for tracing multiple objects in video sequences. In Computer Vision - 14th European Conference, ECCV 2016, Proceedings (Vol. 9910 LNCS, pp. 851-867). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9910 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46466-4_51