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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/753trptklbb4nj6jquqadzwwdu" style="color: black;">2011 International Conference on Computer Vision</a>
Multiple object tracking has been formulated recently as a global optimization problem, and solved efficiently with optimal methods such as the Hungarian Algorithm. A severe limitation is the inability to model multiple objects that are merged into a single measurement, and track them as a group, while retaining optimality. This work presents a new graph structure that encodes these multiple-match events as standard one-to-one matches, allowing computation of the solution in polynomial time.<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iccv.2011.6126532">doi:10.1109/iccv.2011.6126532</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/iccv/HenriquesCB11.html">dblp:conf/iccv/HenriquesCB11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3azlb3gvsbblbg46ivsgri2v4i">fatcat:3azlb3gvsbblbg46ivsgri2v4i</a> </span>
more »... ce identities are lost when objects merge, an efficient method to identify groups is also presented, as a flow circulation problem. The problem of tracking individual objects across groups is then posed as a standard optimal assignment. Experiments show increased performance on the PETS 2006 and 2009 datasets compared to state-of-the-art algorithms.
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