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) |
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DOIs | |
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 |
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Country/Territory | United Kingdom |
City | Leeds |
Period | 9/1/08 → 9/4/08 |
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition