Multi-target data association by tracklets with unsupervised parameter estimation

Weina Ge; Robert T. Collins

doi:10.5244/C.22.93

Multi-target data association by tracklets with unsupervised parameter estimation

Weina Ge
, Robert T. Collins

Computer Science and Engineering

Research output: Contribution to conference › Paper › peer-review

35 Scopus citations

Abstract

We consider multi-target tracking via probabilistic data association among tracklets (trajectory fragments), a mid-level representation that provides good spatio-temporal context for efficient tracking. Model parameter estimation and the search for the best association among tracklets are unified naturally within a Markov Chain Monte Carlo sampling procedure. The proposed approach is able to infer the optimal model parameters for different tracking scenarios in an unsupervised manner.

Original language	English (US)
DOIs	https://doi.org/10.5244/C.22.93
State	Published - 2008
Event	2008 19th British Machine Vision Conference, BMVC 2008 - Leeds, United Kingdom Duration: Sep 1 2008 → Sep 4 2008

Other

Other	2008 19th British Machine Vision Conference, BMVC 2008
Country/Territory	United Kingdom
City	Leeds
Period	9/1/08 → 9/4/08

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition

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10.5244/C.22.93

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title = "Multi-target data association by tracklets with unsupervised parameter estimation",

abstract = "We consider multi-target tracking via probabilistic data association among tracklets (trajectory fragments), a mid-level representation that provides good spatio-temporal context for efficient tracking. Model parameter estimation and the search for the best association among tracklets are unified naturally within a Markov Chain Monte Carlo sampling procedure. The proposed approach is able to infer the optimal model parameters for different tracking scenarios in an unsupervised manner.",

author = "Weina Ge and Collins, \{Robert T.\}",

year = "2008",

doi = "10.5244/C.22.93",

language = "English (US)",

note = "2008 19th British Machine Vision Conference, BMVC 2008 ; Conference date: 01-09-2008 Through 04-09-2008",

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TY - CONF

T1 - Multi-target data association by tracklets with unsupervised parameter estimation

AU - Ge, Weina

AU - Collins, Robert T.

PY - 2008

Y1 - 2008

N2 - We consider multi-target tracking via probabilistic data association among tracklets (trajectory fragments), a mid-level representation that provides good spatio-temporal context for efficient tracking. Model parameter estimation and the search for the best association among tracklets are unified naturally within a Markov Chain Monte Carlo sampling procedure. The proposed approach is able to infer the optimal model parameters for different tracking scenarios in an unsupervised manner.

AB - We consider multi-target tracking via probabilistic data association among tracklets (trajectory fragments), a mid-level representation that provides good spatio-temporal context for efficient tracking. Model parameter estimation and the search for the best association among tracklets are unified naturally within a Markov Chain Monte Carlo sampling procedure. The proposed approach is able to infer the optimal model parameters for different tracking scenarios in an unsupervised manner.

UR - https://www.scopus.com/pages/publications/84898479945

UR - https://www.scopus.com/pages/publications/84898479945#tab=citedBy

U2 - 10.5244/C.22.93

DO - 10.5244/C.22.93

M3 - Paper

AN - SCOPUS:84898479945

T2 - 2008 19th British Machine Vision Conference, BMVC 2008

Y2 - 1 September 2008 through 4 September 2008

ER -

Multi-target data association by tracklets with unsupervised parameter estimation

Abstract

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All Science Journal Classification (ASJC) codes

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