Automated License Plate Recognition for Resource-Constrained Environments

Padmasiri, Heshan; Shashirangana, Jithmi; Meedeniya, Dulani; Rana, Omer; Perera, Charith

doi:10.3390/s22041434

Cited by 36 publications

(12 citation statements)

References 69 publications

(119 reference statements)

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“…On the other hand, there is another very interesting approach that consists of deploying these systems for low-resource devices in real time, emulating real environments. For example, in [41], the authors run their tool in a CPU-based system with 8 GB of RAM, and obtain a precision of 66.1% with MobileNet SSDv2 [42] with a 27.2 FPS rate; or in [43], with metrics of detection of 90% and a recognition rate of 98.73% with just Raspberry Pi3B+ as hardware support. Going further, we can find Android-based systems such as [44] or [45], which are designed to be deployed in mobile phones and operate in real time.…”

Section: Up-to-date Solutionsmentioning

confidence: 99%

“…Under these circumstances, it may be better to opt for less precise but less resourceconsuming systems. For example, some ALPR systems are ready to operate through CPU instead of GPU, although they sacrifice precision; such as [41] or [43]. It is important to mention that in critical scenarios, there will always be a human controller behind the system, so these tools may deliver more false positives rather than false negatives (and the human controller will discriminate if it is right or not).…”

Section: Speed Processing and Computational Costsmentioning

confidence: 99%

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Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

2022

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Security cameras have been proven to be particularly useful in preventing and combating crime through identification tasks. Here, two areas can be mainly distinguished: person and vehicle identification. Automatic license plate readers are the most widely used tool for vehicle identification. Although these systems are very effective, they are not reliable enough in certain circumstances. For example, due to traffic jams, vehicle position or weather conditions, the sensors cannot capture an image of the entire license plate. However, there is still a lot of additional information in the image which may also be of interest, and that needs to be analysed quickly and accurately. The correct use of the processing mechanisms can significantly reduce analysis time, increasing the efficiency of video cameras significantly. To solve this problem, we have designed a solution based on two technologies: license plate recognition and vehicle re-identification. For its development and testing, we have also created several datasets recreating a real environment. In addition, during this article, it is also possible to read about some of the main artificial intelligence techniques for these technologies, as they have served as the starting point for this research.

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Section: Up-to-date Solutionsmentioning

confidence: 99%

Section: Speed Processing and Computational Costsmentioning

confidence: 99%

Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

2022

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“…Another method has been to use computer vision techniques to detect licence plate numbers. There are many challenges [9] to detecting vehicle licence plates covertly in remote jungle areas, such as limited visibility and lighting [10], limited connectivity, limited access to computation and energy sources [11], exposure to harsh jungle environments (e.g. humidity, water etc.)…”

Section: Vehicle Tracking Using Rfid/ble/vision Technologymentioning

confidence: 99%

Low-Cost SMS Driven Location Tracking Platform Towards Anti-Poaching Efforts

Burkett¹,

Wengel²,

Goossens³

et al. 2022

Preprint

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Throughout the world, poaching has been an ever-present threat to a vast array of species for over many decades. Traditional anti-poaching initiatives target catching the poachers. However, the challenge is far more complicated than catching individual poachers. Poaching is an industry which needs to be fully investigated. Many stakeholders are directly and indirectly involved in poaching activities (e.g., some local restaurants illegally providing meat to tourists). Therefore, stopping or severely decapitating the poaching industry requires a unified understanding of all stakeholders. The best way to uncover these geographical and social relationships is to track the movements of poachers. However, location tracking is challenging in most rural areas where wildlife sanctuaries are typically located. Internet-connected communication (e.g. 3G) technologies typically used in urban cities are not feasible in these rural areas. Therefore, we decided to develop an SMS (short message service) base low-cost tracking system (SMS-TRACCAR) to track poachers. The proposed system was developed to be deployed in Kinabatangan Wildlife Sanctuary, Sabah, Malaysia and nearby villages and cities where poachers typically move around. Our evaluations demonstrated that SMS-based tracking could provide sufficient quality (granular) data (with minimum energy consumption) that enable us to monitor poacher vehicle movements within rural areas where no other modern communication technologies are feasible to use. However, it is important to note that our system can be used in any domain that requires SMS-based geo-location tracking. SMS-TRACCAR can be configured to track individuals as well as groups. Therefore, SMS-TRACCAR contributes not only to the wildlife domain but in the wider context as well.

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“…Due to the variety of conditions and types of license plates, the capacity to automatically detect and recognize plates is one of the essential instruments employed by police department organizations worldwide. Despite common opinion, license plate isolation and classification remain a difficult challenge [4]. The majority of existing options are fundamentally constrained.…”

Section: Introductionmentioning

confidence: 99%

Detection and Recognition of Car Plates in Parking Lots at Baghdad University

Abbas

Mohamad²

2023

Iraqi Journal of Science

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A simple and smart algorithm was presented to recognize car plates in parking at the College of Science for Women, University of Baghdad, Iraq. The study consists of recording video clips of all cars parked in the selected area. The studied camera heights were1m and 2m, and the video clips were 19 and 30. Images were extracted from the video clip to be used for training data for the cascade method. Haar classification was used to detect license plates after the training step. Viola-jones algorithm was applied to the output of Haar’s data for both camera heights (1m and 2m). The accuracy was calculated for all data with different weather conditions and local time recoding. The accuracy is 100% for all data in this study.

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Automated License Plate Recognition for Resource-Constrained Environments

Cited by 36 publications

References 69 publications

Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

Low-Cost SMS Driven Location Tracking Platform Towards Anti-Poaching Efforts

Detection and Recognition of Car Plates in Parking Lots at Baghdad University

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