@inProceedings{
 title = {Multi-object tracking using binary masks.},
 type = {inProceedings},
 year = {2008},
 pages = {2640-2643},
 id = {482e3dc1-c66d-303e-b2ac-b0400b8d2197},
 created = {2019-11-19T13:01:23.414Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {17585b85-df99-3a34-98c2-c73e593397d7},
 last_modified = {2019-11-19T13:47:32.855Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {mvg:1205},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {In this paper, we introduce a new method for tracking multiple objects. The method combines Kalman filtering and the Expectation Maximization (EM) algorithm in a novel way to deal with observations that obey a Gaussian mixture model instead of a unimodal distribution that is assumed by the ordinary Kalman filter. It also involves a new approach to measuring the object locations using a series of morphological operations with binary masks. The benefit of this approach is that soft assignment of the measurements to corresponding objects can be performed automatically using their a posteriori probabilities. This is a general approach for multi-object tracking, and there are basically various ways to segment the objects, but in this paper we use simple color features simply to demonstrate the feasibility of the concept.},
 bibtype = {inProceedings},
 author = {J, Huttunen S & Heikkilä},
 booktitle = {Proc. 15th International Conference on Image Processing (ICIP 2008), San Diego}
}

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