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

Han Ul Kim; Chang-Su Kim

doi:10.1007/978-3-319-46466-4_51

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 proceeding › Conference contribution

31 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 language	English
Title of host publication	Computer Vision - 14th European Conference, ECCV 2016, Proceedings
Editors	Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
Publisher	Springer Verlag
Pages	851-867
Number of pages	17
ISBN (Print)	9783319464657
DOIs	https://doi.org/10.1007/978-3-319-46466-4_51
Publication status	Published - 2016
Event	14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands Duration: 2016 Oct 8 → 2016 Oct 16

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9910 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th European Conference on Computer Vision, ECCV 2016
Country/Territory	Netherlands
City	Amsterdam
Period	16/10/8 → 16/10/16

Bibliographical note

Funding Information:
This work was supported partly by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF- 2015R1A2A1 A10055037), and partly by the MSIP, Korea, under the ITRC support program supervised by the Institute for Information & communications Technology Promotion (No. IITP-2016-R2720-16-0007).

Publisher Copyright:
© Springer International Publishing AG 2016.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

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

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-46466-4_51

Cite this

Kim, H. U., & Kim, C.-S. (2016). CDT: Cooperative detection and tracking for tracing multiple objects in video sequences. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Computer Vision - 14th European Conference, ECCV 2016, Proceedings (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

CDT: Cooperative detection and tracking for tracing multiple objects in video sequences. / Kim, Han Ul; Kim, Chang-Su.
Computer Vision - 14th European Conference, ECCV 2016, Proceedings. ed. / Bastian Leibe; Jiri Matas; Nicu Sebe; Max Welling. Springer Verlag, 2016. p. 851-867 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9910 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, HU & Kim, C-S 2016, CDT: Cooperative detection and tracking for tracing multiple objects in video sequences. in B Leibe, J Matas, N Sebe & M Welling (eds), Computer Vision - 14th European Conference, ECCV 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9910 LNCS, Springer Verlag, pp. 851-867, 14th European Conference on Computer Vision, ECCV 2016, Amsterdam, Netherlands, 16/10/8. https://doi.org/10.1007/978-3-319-46466-4_51

Kim HU, Kim CS. CDT: Cooperative detection and tracking for tracing multiple objects in video sequences. In Leibe B, Matas J, Sebe N, Welling M, editors, Computer Vision - 14th European Conference, ECCV 2016, Proceedings. Springer Verlag. 2016. p. 851-867. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46466-4_51

Kim, Han Ul ; Kim, Chang-Su. / CDT : Cooperative detection and tracking for tracing multiple objects in video sequences. Computer Vision - 14th European Conference, ECCV 2016, Proceedings. editor / Bastian Leibe ; Jiri Matas ; Nicu Sebe ; Max Welling. Springer Verlag, 2016. pp. 851-867 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1d669d7286414561b68bd7d614bdc2a2,

title = "CDT: Cooperative detection and tracking for tracing multiple objects in video sequences",

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.",

keywords = "Joint detection and tracking, Model-free tracking, Multiple object tracking, Object detection, Online multi-object tracking",

author = "Kim, {Han Ul} and Chang-Su Kim",

note = "Funding Information: This work was supported partly by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF- 2015R1A2A1 A10055037), and partly by the MSIP, Korea, under the ITRC support program supervised by the Institute for Information & communications Technology Promotion (No. IITP-2016-R2720-16-0007). Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 14th European Conference on Computer Vision, ECCV 2016 ; Conference date: 08-10-2016 Through 16-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46466-4_51",

language = "English",

isbn = "9783319464657",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "851--867",

editor = "Bastian Leibe and Jiri Matas and Nicu Sebe and Max Welling",

booktitle = "Computer Vision - 14th European Conference, ECCV 2016, Proceedings",

}

TY - GEN

T1 - CDT

T2 - 14th European Conference on Computer Vision, ECCV 2016

AU - Kim, Han Ul

AU - Kim, Chang-Su

N1 - Funding Information: This work was supported partly by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF- 2015R1A2A1 A10055037), and partly by the MSIP, Korea, under the ITRC support program supervised by the Institute for Information & communications Technology Promotion (No. IITP-2016-R2720-16-0007). Publisher Copyright: © Springer International Publishing AG 2016. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Joint detection and tracking

KW - Model-free tracking

KW - Multiple object tracking

KW - Object detection

KW - Online multi-object tracking

UR - http://www.scopus.com/inward/record.url?scp=84990050217&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84990050217&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46466-4_51

DO - 10.1007/978-3-319-46466-4_51

M3 - Conference contribution

AN - SCOPUS:84990050217

SN - 9783319464657

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 851

EP - 867

BT - Computer Vision - 14th European Conference, ECCV 2016, Proceedings

A2 - Leibe, Bastian

A2 - Matas, Jiri

A2 - Sebe, Nicu

A2 - Welling, Max

PB - Springer Verlag

Y2 - 8 October 2016 through 16 October 2016

ER -

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this