When calculating the shortest path adjacent track points between the candidate points, for convenience, we use Dirjkstra algorithm.
Based on this, the algorithm stores only the first five candidate points, and the segments where the distance between the candidate points and the track points is the smallest in the database. .
Based on the existing map-matching algorithm geometry, it shows that if the distance between the candidate points and the locus of points are closer, the greater the likelihood of the candidate point is the best match point.
For a certain facial landmark, Adaboost classifier outputs a response depicting the similarity between the representations of the candidate points compared to the learned training model.
A kernel correlation analysis approach is proposed to find the detection likelihood by maximizing a similarity criterion between the target points and the candidate points.
It provides a consistent way to resolve the ambiguities that arise in associating multiple objects with measurements of the similarity criterion between the target points and the candidate points.
Here, after sorting all of the candidate points based on their D X ^ value, the range between the minimum and maximum values is partitioned into ten sections, and the candidate points that fall in the first section are discarded.
Set of the candidate points for recovery centers, ∀e ∊ E.
is the "global" correlation value, computed between two windows, centered, respectively, in the starting point and in the candidate point of the column ; is the "local" correlation value, computed between two windows, centered, respectively, in the last included point for the previous column, and in the candidate point of the column.
(ii) is the "global" correlation value, computed between two windows, centered, respectively, in the starting point and in the candidate point of the column ; (iii) is the "local" correlation value, computed between two windows, centered, respectively, in the last included point for the previous column, and in the candidate point of the column.
V ' is the candidate point of the track point; T ' is the side represented by the shortest path between two adjacent candidate points.
