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stands for all the number of character char i occurance appeared on LPs un-
der weather w in the time period of t . Since RFID detector are accurate to
be convincible, N ( char i |
w,t ) would be generated according to RFID detector
results. Accordingly, N ( char i
w,t ) stands for the number of that char-
acter char i transforms into char j occurance recognized by ALPR under weather
w in the time period of t .
Then given character char i and char j , respectively stand for real character
and recognized character at the same position in a LP , char i ,char j
char j |
Char Set .
And w
Weather Set,t
Time Set ,let
f ( char i ,char j ,w,t )= N ( char i
char j |
w,t )
(4)
N ( char i )
Here, N ( char i ) stands for all the number of character char i occurance ap-
peared on the LP taken by ca i . Formula (4) means that the character transition
probability of char i transforms into char j under weather w in the time period
of t .
CTPT is a table which has five columns including real character, recognized
character, weather, time period and transition probability. By Formula (4), we
can generate a CTPT for every ca i , ca i ∈ CA for all weather conditions and
time periods. CTPT would be updated in a certain period ˃ , and in this paper,
we set ˃ as one week temporarily. In the further research, ˃ would be discussed
in detail.
SLPs
Correction. After Algorithm 1 is finished, most of correctly recognized
plates have been identified. For those plates that are not marked as TLP in
Algorithm 1, we deem them as SLPs .A SLP is not definitely an incorrectly
recognized plate, just because the high probability it has. However, if this SLP
is indeed incorrectly recognized, its corresponding correct plate should occur in
its k -degree neighbors multiple times for some k .
Now, we define the similarity comparing LP to other LPs . Suppose there
are n characters in LP and char i ( LP ) stands for the i th character of LP .The
similarity of LP and LP recognized by camera ca is described as follows:
sim ( LP,LP )= n
P ( char i ( LP ) ₒ char i ( LP ) |w,t )
(5)
i =1
w,t ) stands for the probability of the i th
character of LP transforms into the i th character of LP under the weather w in
the time period t . According to Nave Bayes theory ,
char i ( LP )
Here, P ( char i ( LP )
|
sim ( LP, LP )= i =1 CTPT ca ( char i ( LP ) ,char i ( LP ) ,w,t ) ∗ N ( char i ( LP ) |w,t )
N ( char i ( LP ) |w,t )
(6)     Search WWH ::

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