Abstract
Low-quality surveillance cameras throughout the cities could provide important cues to identify a suspect, for example, in a crime scene. However, the license-plate recognition is especially difficult under poor image resolutions. In this vein, super-resolution (SR) can be an inexpensive solution, via software, to overcome this limitation. Consecutive frames in a video may contain different information that could be integrated into a single image, richer in details. In this paper, we design and develop a novel, free and open-source framework underpinned by SR and automatic license-plate recognition (ALPR) techniques to identify license-plate characters in low-quality real-world traffic videos, captured by cameras not designed specifically for the ALPR task, aiding forensic analysts in understanding an event of interest. The framework handles the necessary conditions to identify a target license plate, using a novel methodology to locate, track, align, super-resolve, and recognize its alphanumerics. The user receives as outputs the rectified and super-resolved license-plate, richer in detail, and also the sequence of license-plates characters that have been automatically recognized in the super-resolved image. Additionally, we also design and develop a novel SR method that projects the license-plates separately onto the rectified grid, and then fill in the missing pixels using inpainting techniques. We compare the different algorithms in the framework (five for tracking, three for registration, seven for reconstruction, two for post-processing, and two for the recognition step), and present discussions on the pros and cons of each choice. Our experiments show that SR can indeed increase the number of correctly recognized characters posing the framework as an important step toward providing forensic experts and practitioners with a solution for the license-plate recognition problem under difficult acquisition conditions.
Highlights
Automatic license-plate recognition (ALPR) uses optical character recognition (OCR) on images to extract and recognize the characters of a vehicle registration plate [1], [2]
We describe how we adapt them to the license-plate recognition problem we have in this paper
Since the number of hits is higher in the reconstructed images, we may claim that our initialization step is providing an appropriate region of interest (ROI) for the super-resolution
Summary
Automatic license-plate recognition (ALPR) uses optical character recognition (OCR) on images to extract and recognize the characters of a vehicle registration plate [1], [2]. It is usually aided by cameras designed for such task, since the license-plate recognition may be especially difficult under poor images resolutions (usually when the car is too far away from the camera, under adverse atmospheric conditions, or due to a low-quality acquisition camera) [3]. There are a number of low-quality surveillance cameras scattered throughout our cities that could help to identify a suspect, for example, in a crime scene. As a matter of fact, there exist some techniques in the literature that leverage side information
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
