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k =1 |
n
d a = 1
n
( S a ) k
( S b ) k |
(12)
where P a and P b are main points sets of two trajectories a and b, S a and S b are
main segments sequences of them.
Fine classification also uses modified k-means method to subdivide classes.
Position distance d p and acceleration distance d a are constrains of this fine clas-
sification. The more specific algorithm is shown in Algorithm 3.
Algorithm 3. Fine classification ( d p + d a )
Input:
Class : Trajectories belong to this class
Output:
List of subclasses
Processing:
Find out trajectories whose d p + d a are the most large ones to be K centroid.
for T a ∈ Class do
TempDis = d p ( T a ,Cid 1 )+ d a ( T a ,Cid 1 );
for i =2; i ≤ K ; i ++ do
if TempDis>d p ( T a ,Cid i )+ d a ( T a ,Cid i ) then
TempDis = d p ( H a ,Cid i )+ d a ( T a ,Cid i );
C a = i ;
end if
end for
if TempDis>d pamin then
K = K +1;
H a belongs to the new K th subclass;
else
Then T a belongs to the C a th subclass, m C a is the number of trajectories belongs
to the C a th subclass;
position centroid:
CidP C a =
m C a
m C a +1
1
m C a +1
CidP C a +
P La
acceleration centroid:
CidA C a =
m C a
m C a +1
1
m C a +1
CidA C a +
S Aa
m C a = m C a +1;
end if
end for
2.3 Gaussian Process Regression for Matching
In order to find out similar ones for all kinds of trajectories, we adopt Gaus-
sian Process Regression method [3] to build the model a = f ( q )+ τ ,where
τ
∼ℵ
( μ,σ 2 ). q denotes the position of point ( q x ,q y ). The position sequence is
q =
. a is the acceleration of the point, that includes a x and a y
in different direction. The acceleration sequence is a =
{
q 1 ,q 2 ,...,q n }
.Inthis
part,we train Gaussian Progress Regression model for both acceleration a x and
{
a 1 ,a 2 ,...,a n }
 
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