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Probabilistic multiple hypothesis tracking (PMHT) is an efficient approach for dealing with it.
Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization.
The inherent ambiguity of the data makes the use of adequate algorithms, such as multiple hypothesis tracking, inevitable.
A data association method, either Global Nearest Neighbor or Multiple Hypothesis Tracking, was employed to associate uncertain measurements to known tracks.
One of the first approaches focusing on MTT problem is the Multiple Hypothesis Tracking (MHT) algorithm [6], which maintains several correspondence hypotheses for each object at each frame.
Other tracking methods using Interacting Multiple Model (IMM) and Multiple Hypothesis Tracking (MHT) techniques could be used to refine detection and data association [3].
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Multiple hypothesis tracking-based data association is included to be able to deal with ambiguous scenarios.
The most widely used target association techniques include joint probability data association filtering (JPDAF) [18 21], multiple-hypothesis tracking (MHT) [22 25], and flow network framework (FNF) [26 29].
Subsequently, an optimization strategy, such as multiple-hypothesis tracking (Chenouard et al., 2013), integer programming (Li et al., 2010), dynamic programming (Magnusson and Jaldén, 2012) or coupled minimum-cost flow tracking (Padfield et al., 2011), is used to determine the most likely cell correspondence between frames.
Friman [ 15] proposed multiple hypothesis template tracking, which follows the direction of centerline obtained in advance.
Most of the conventional algorithms about multitarget tracking, such as multiple hypothesis tracker (MHT) [12], joint probabilistic data association (JPDA) [13, 14], and probability hypothesis density (PHD) filter [15], assume the following measurement model: (1) every target produces at most one measurement and (2) any measurement is produced by a target or clutter.
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