A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Anytime learning for the NoSLLiP tracker
2009
Image and Vision Computing
We propose an anytime learning procedure for the Sequence of Learned Linear Predictors (SLLiP) tracker. Since learning might be time-consuming for large problems, we present an anytime learning algorithm which, after a very short initialization period, provides a solution with defined precision. As SLLiP tracking requires only a fraction of the processing power of an ordinary PC, the learning can continue in a parallel background thread continuously delivering improved, i.e. faster, SLLiPs with
doi:10.1016/j.imavis.2009.03.005
fatcat:rv4ahpksxbcbxg2wpbfa5d6f5y