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License
Plate Detection and Recognition for Degraded CCTV Videos |
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1. Introduction
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Intelligent
transportation systems (ITSs) aim at improving transportation safety and
productivity.
l
ITSs make use of advanced
technologies such automatic license plate recognition (LPR) that has recently
attracted considerable amount of research attention.
LPR applications:
tolling, border control, traffic control, marketing tools, and airport parking.
l
Challenges in LPR in
CCTV: degraded video frames
The size of license
plate (LP) region is under 80x20 pixels.
In such a case, the
size of each plate character is usually 8x12 pixels.
Blur and noise: blur
usually comes from motion of vehicles while noise is generated from dust and
vehicle exhaust.
l
We propose an LPR
system for degraded vehicle images in real-world applications.
We propose a
character segmentation method using prior-knowledge.
We propose an
enhanced character feature which exploits Gabor wavelet with color information,
which is robust to low-resolution video frames.
We propose simple and
effective multiple-frame-based character recognition based on probabilistic
voting.
2. License Plate Detection
Fig. 1. License plate detection from
CCTV image by using color and texture information
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(a) |
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(b) |
Fig. 2. An example
of color partition.
(a) Illustration for HSV color space and color partition.
(b) White, Black, and Gray colors represent background, foreground of LP region,
and background of LP region.
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There are two types
of LPs i.e., white-black type and green-white type.
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Based on color
partition, we count the color transitions between pixels of LP foreground color
and pixels of LP background color.
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If the counted transition
number (within a 15x3 window) is higher than the pre-determined threshold
value, then the center pixel of the window is regarded as LP candidate.
3. Character Segmentation
Fig. 3.
Procedure of extracting LP character regions from detected LP region.
Fig. 4. Initial LP character
detection using SVM classifier
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Initial LP character
detection: We first examine every candidate character region by using support
vector machine (SVM) classifier.
The only character
regions which has higher confidence value than a threshold value is regarded as
the initial LP characters.
l
We detect the
remaining LP character regions using the assumption that missing character
should be in the left or right side from the initial LP characters.
4. Character Recognition
Fig. 5. Color Gabor feature
extraction from LP character region.
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(a) |
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Fig. 6. Multiple-frame-based
LP character recognition.
(a) Example of combining the results from multiple frames.
(b) Probabilistic voting for LP character recognition.
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We first select most
frequently occurred candidates (e.g., 7624 and 6241 in Fig. 6(a)) among all
possible 4bit string.
l
To select the most
probable candidate for the vehicle identification number, among the candidates
in the above step, we use probabilistic voting.
4 confidence values
from 4bit string are summed and compared to find the candidate with the highest
probability.
5. Experiment
5.1 License Plate Detection Results
<Experimental
setup>
l Datasets
Dataset 1: Contains 203 vehicle images with LPs of green-white type (see Fig. 7(a))
Dataset 2: Contains 224 vehicle images with LPs of white-black type (see Fig. 7(b))
l Experimental condition
Vehicle images were captured by using a CCTV camera
Camera resolution: 640x480
LP size: over 80 pixels in horizontal size
Time for image acquisition: 3 PM
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(a) |
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(b) |
Fig. 7. Example vehicle images
used in experiment.
(a) for green-white LP. (b) for
white-black LP.
<Experimental
results>
Table 1. License
plate detection performance.
Data |
Feature |
Plate Detection Rate |
Dataset 1 |
Green |
95.07% (193/203) |
Dataset 2 |
White |
90.18% (202/224) |
Total |
92.51% (395/427) |
5.2 License Plate Recognition
Results
<Experimental
setup>
l Datasets
Dataset 1: Contains 656 character images from LPs of green-white type
Dataset 2: Contains 737 character images from LPs of white-black type
<Experimental
results>
Table 2. Character
recognition performance.
Character feature |
Dataset 1 |
Dataset 2 |
Raw pixel data |
87.48% (587/656) |
86.16% (635/737) |
Binary feature |
90.09% (591/656) |
87.38% (644/737) |
Local Binary Pattern |
60.67% (398/656) |
58.89% (434/737) |
Gabor wavelet |
96.95% (636/656) |
95.25% (702/737) |
Color Gabor wavelet
(proposed) |
97.87% (642/656) |
95.39% (703/737) |
Table 3. Comparison between single-frame-based approach and
multiple-frame-based approach for 4 digit LP recognition.
Character feature |
Using single-frame-based approach (baseline) |
Using multiple-frame-based approach (proposed) |
Dataset 1 |
54.92% (106/193) |
78.95% (30/38) |
Dataset 2 |
53.96% (109/202) |
76.36% (42/55) |
6. Conclusions
l In this research, we proposed an improved LPR system that can be
effectively applied to real-world environment.
l
For LP character segmentation, we introduce ROI
calibration after initial character detection to find missing characters.
We achieved improved character segmentation result
even if the vehicle images were blurred and noise-corrupted.
l
In license plate
recognition, we proposed to incorporate the vehicle images of same vehicle to
perform probabilistic voting, yielding successful LPR result compared to the
conventional single frame-based approach.
* Contact Person: Prof. Yong Man Ro (ymro@kaist.ac.kr)
1. Hyun-Seok Min, Seung Ho Lee, Wesley De Neve and Yong Man Ro ¡°Sparse Representation-based Super-Resolution for Improved License Plate Recognition for Low-Resolution CCTV Forensics,¡± International Workshop on Digital-Forensic and Watermarking (IWDW), 2013.
2. ½Å¿íÁø, À̽ÂÈ£, ¹ÎÇö¼®, ÃÖÀ翵, ³ë¿ë¸¸, ¡°±â¿ï±â¿Í Å©±â º¯È¿¡ °ÀÎÇÑ Â÷·® ¹øÈ£ÆÇ °ËÃâ¿¡ °üÇÑ ¿¬±¸,¡± Çѱ¹¸ÖƼ¹Ìµð¾îÇÐȸ Ãß°èÇмú¹ßÇ¥´ëȸ, 2011.
l Video1: License plate
detection and recognition in CCTV video .
l Video2: Intelligent
Surveillance System Using the Proposed License Plate Recognition .