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A data association method, either Global Nearest Neighbor or Multiple Hypothesis Tracking, was employed to associate uncertain measurements to known tracks.
A data association algorithm is applied to maintain tracking of multiple objects under circumstances.
In this context, an open fundamental question is how a data association mechanism can integrate bottom up sensory information and top down knowledge.
Brown and Hagen [27] proposed a data association method for linking criminal records that possibly refer to the same suspect.
Given that the tracking algorithm is preceded by a data association algorithm, we can analyze the activity performed by each individual separately.
Since the state vector for target consists of the states for all body parts, a data association problem needs to be resolved.
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This part of algorithm represents a data-association phase.
Our method relies on a data-association framework by resolving a MAP problem.
Our clustering method is based on a probabilistic maximum a posteriori data association framework, and we apply it to face detection in a visual surveillance context.
This approach extends recent work which formulates the tracking-by-detection into a maximum-a posteriori (MAP) data association problem.
As a result, data association samples are drawn from a t k ~ p ( a t ) d e t ( 2 π Σ f ) f ( x t ; a t ) *, (30).
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Justyna Jupowicz-Kozak
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