Enhancing Linear Programming with Motion Modeling for Multi-target Tracking

Niall McLaughlin; Jesus Martinez Del Rincon; Paul Miller

doi:10.1109/WACV.2015.17

Enhancing Linear Programming with Motion Modeling for Multi-target Tracking

Niall McLaughlin, Jesus Martinez Del Rincon, Paul Miller

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

40 Citations (Scopus)

693 Downloads (Pure)

Abstract

In this paper we extend the minimum-cost network flow approach to multi-target tracking, by incorporating a motion model, allowing the tracker to better cope with longterm occlusions and missed detections. In our new method, the tracking problem is solved iteratively: Firstly, an initial tracking solution is found without the help of motion information. Given this initial set of tracklets, the motion at each detection is estimated, and used to refine the tracking solution.
Finally, special edges are added to the tracking graph, allowing a further revised tracking solution to be found, where distant tracklets may be linked based on motion similarity. Our system has been tested on the PETS S2.L1 and Oxford town-center sequences, outperforming the baseline system, and achieving results comparable with the current state of the art.

Original language	English
Title of host publication	2015 IEEE Winter Conference on Applications of Computer Vision (WACV)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	71-77
Number of pages	7
ISBN (Print)	978-1-4799-6682-0
DOIs	https://doi.org/10.1109/WACV.2015.17
Publication status	Published - 06 Jan 2015
Event	WACV 2015: IEEE Winter Conference on Applications of Computer Vision - Hawaii, Waikoloa Beach, United States Duration: 06 Jan 2015 → 09 Jan 2015

Conference

Conference	WACV 2015: IEEE Winter Conference on Applications of Computer Vision
Country/Territory	United States
City	Waikoloa Beach
Period	06/01/2015 → 09/01/2015

Access to Document

10.1109/WACV.2015.17

Enhancing Linear Programming with Motion Modeling for Multi-target Tracking
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Accepted author manuscript, 2.2 MB

Cite this

@inproceedings{ee107a91dc56487c9cda0454a815e100,

title = "Enhancing Linear Programming with Motion Modeling for Multi-target Tracking",

abstract = "In this paper we extend the minimum-cost network flow approach to multi-target tracking, by incorporating a motion model, allowing the tracker to better cope with longterm occlusions and missed detections. In our new method, the tracking problem is solved iteratively: Firstly, an initial tracking solution is found without the help of motion information. Given this initial set of tracklets, the motion at each detection is estimated, and used to refine the tracking solution.Finally, special edges are added to the tracking graph, allowing a further revised tracking solution to be found, where distant tracklets may be linked based on motion similarity. Our system has been tested on the PETS S2.L1 and Oxford town-center sequences, outperforming the baseline system, and achieving results comparable with the current state of the art.",

author = "Niall McLaughlin and {Martinez Del Rincon}, Jesus and Paul Miller",

year = "2015",

month = jan,

day = "6",

doi = "10.1109/WACV.2015.17",

language = "English",

isbn = "978-1-4799-6682-0",

pages = "71--77",

booktitle = "2015 IEEE Winter Conference on Applications of Computer Vision (WACV)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "WACV 2015: IEEE Winter Conference on Applications of Computer Vision ; Conference date: 06-01-2015 Through 09-01-2015",

}

McLaughlin, N , Martinez Del Rincon, J & Miller, P 2015, Enhancing Linear Programming with Motion Modeling for Multi-target Tracking. in 2015 IEEE Winter Conference on Applications of Computer Vision (WACV). Institute of Electrical and Electronics Engineers Inc., pp. 71-77, WACV 2015: IEEE Winter Conference on Applications of Computer Vision, Waikoloa Beach, United States, 06/01/2015. https://doi.org/10.1109/WACV.2015.17

Enhancing Linear Programming with Motion Modeling for Multi-target Tracking. / McLaughlin, Niall ; Martinez Del Rincon, Jesus ; Miller, Paul.

2015 IEEE Winter Conference on Applications of Computer Vision (WACV). Institute of Electrical and Electronics Engineers Inc., 2015. p. 71-77.

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

TY - GEN

T1 - Enhancing Linear Programming with Motion Modeling for Multi-target Tracking

AU - McLaughlin, Niall

AU - Martinez Del Rincon, Jesus

AU - Miller, Paul

PY - 2015/1/6

Y1 - 2015/1/6

N2 - In this paper we extend the minimum-cost network flow approach to multi-target tracking, by incorporating a motion model, allowing the tracker to better cope with longterm occlusions and missed detections. In our new method, the tracking problem is solved iteratively: Firstly, an initial tracking solution is found without the help of motion information. Given this initial set of tracklets, the motion at each detection is estimated, and used to refine the tracking solution.Finally, special edges are added to the tracking graph, allowing a further revised tracking solution to be found, where distant tracklets may be linked based on motion similarity. Our system has been tested on the PETS S2.L1 and Oxford town-center sequences, outperforming the baseline system, and achieving results comparable with the current state of the art.

AB - In this paper we extend the minimum-cost network flow approach to multi-target tracking, by incorporating a motion model, allowing the tracker to better cope with longterm occlusions and missed detections. In our new method, the tracking problem is solved iteratively: Firstly, an initial tracking solution is found without the help of motion information. Given this initial set of tracklets, the motion at each detection is estimated, and used to refine the tracking solution.Finally, special edges are added to the tracking graph, allowing a further revised tracking solution to be found, where distant tracklets may be linked based on motion similarity. Our system has been tested on the PETS S2.L1 and Oxford town-center sequences, outperforming the baseline system, and achieving results comparable with the current state of the art.

UR - http://wacv2015.org/wp-content/uploads/2013/01/WACV_2015_Pocket_Guide_Final.pdf

UR - http://www.ecit.qub.ac.uk/Research/SpeechVisionSystems/Videosurveillance/EnhancingLinearProgrammingwithMotionModelingforMulti-targetTracking/

U2 - 10.1109/WACV.2015.17

DO - 10.1109/WACV.2015.17

M3 - Conference contribution

SN - 978-1-4799-6682-0

SP - 71

EP - 77

BT - 2015 IEEE Winter Conference on Applications of Computer Vision (WACV)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - WACV 2015: IEEE Winter Conference on Applications of Computer Vision

Y2 - 6 January 2015 through 9 January 2015

ER -

Enhancing Linear Programming with Motion Modeling for Multi-target Tracking

Abstract

Conference

Access to Document

Other files and links

Fingerprint

R1118ECI: Centre for Secure Information Technologies (CSIT)

Niall McLaughlin

Cite this

Enhancing Linear Programming with Motion Modeling for Multi-target Tracking

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Projects

R1118ECI: Centre for Secure Information Technologies (CSIT)

Profiles

Niall McLaughlin

Cite this