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Suppose the number of real occurrence of LP appeared in its 0,1,2,..., k -degree
neighbors is ʸ ,when ʸ
ʸ in its 0,1,2,..., k -degree neighbors in interval ʔt ,
LP would be marked as TLP . Algorithm 1, TPLs Extraction, illustrates the
procedures of how TLPs can be identified.
Algorithm 1. TLPs Extraction
Input: The camera set CA , the limit of k when searching k -degree neighbors, the
continuous occurrence limit ʸ ,andtheinterval ʔt between the camera in question
and the k -degree neighbors
Output: The TLPs of each camera ca ∈ CA
1: for each ca ∈ CA do
2:
for each LP recognized by ca do
3:
if ʸ
≥ ʸ in its 0,1,2,..., k -degree neighbors in interval ʔt then
4:
Mark
LP
as
TLP
;
5: end if
6: end for
7: end for
8: return TLPs
Algorithm 1 checks whether ʸ can be satisfied first (line 3), if that does at
some k i <k , TLPs Extraction only does calculate camera ca sk i +1 ,k i +
2 , ..., k degree neighbors. Thus it considerably deceases the time complexity of
Algorithm 1. Assume that each camera has m 1-deree neighbors, if we do not
set the constraint on ʸ , the time complexity of Algorithm 1 will be O nm k .
Now under the constraint of ʸ , Algorithm 1 breaks the inner loop whenever ʸ is
met. Here ʔt is used to filter out vehicles that are not the same as the LP in
question, based on the assumption that vehicles are floating on the roads.
CTPT Generation (Character Transition Probability Table Genera-
tion). The purpose of the process of CTPT Generation is to generate character
transition probability table which is applied for correcting SLPs .Aspointed
out in the beginning of Section 3, we think of the plate as a string. Most ALPR
algorithms recognize each character of the plate independently.
Here, let Char Set stand for the set of all characters recognized by ALPR,
i.e., Char Set =
. We can claim that for an incorrect
plate recognized by ALPR, the position of the misrecognized character does not
depend on other characters being correctly recognized or not. For each camera,
a CTPT is built for predicting the error pattern.
Assume that each camera ca i makes mistakes in its own way, ca i
{
char 1 ,char 2 , ..., char n }
CA ,and
there are many external factors affecting recognition result, such as weather
conditions, light intensity, etc. Let Weather Set =
stand for va-
riety of weather conditions, such as sunny, rainy, foggy, etc. Due to light intensity
changes over time, we divide a day into six time periods for every four hours,
i.e., Time Set =
{
w 1 ,w 2 , ..., w n }
{
t 1 ,t 2 ,t 3 ,t 4 ,t 5 ,t 6 }
.Andto LPs taken by ca i , N ( char i |
w,t )       Search WWH ::

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