Multi-target data association by tracklets with unsupervised parameter estimation

Research output: Contribution to conferencePaper

33 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 languageEnglish (US)
DOIs
StatePublished - Jan 1 2008
Event2008 19th British Machine Vision Conference, BMVC 2008 - Leeds, United Kingdom
Duration: Sep 1 2008Sep 4 2008

Other

Other2008 19th British Machine Vision Conference, BMVC 2008
CountryUnited Kingdom
CityLeeds
Period9/1/089/4/08

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Cite this

Ge, W., & Collins, R. T. (2008). Multi-target data association by tracklets with unsupervised parameter estimation. Paper presented at 2008 19th British Machine Vision Conference, BMVC 2008, Leeds, United Kingdom. https://doi.org/10.5244/C.22.93