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.

Text Reference

Weina Ge and Robert T. Collins, "Multi-target Data Association by Tracklets with Unsupervised Parameter Estimation," British Machine Vision Conference (BMVC), September, 2008

BibTeX Reference

@inproceedings{Ge_BMVC_2008,
    author = "Weina Ge and Robert T. Collins",
    title = "Multi-target Data Association by Tracklets with Unsupervised Parameter Estimation",
    booktitle = "British Machine Vision Conference (BMVC)",
    month = "September",
    year = "2008",
}