Capturing licence plates: police-citizen interaction apps from an EU data protection perspective

Milaj, Jonida; Eck, Gerard Jan Ritsema van

doi:10.1080/13600869.2019.1600335

Cited by 7 publications

(3 citation statements)

References 25 publications

(8 reference statements)

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“…Indeed, use cases for police functions include both obvious upgrades to the profession (e.g. matching individuals in crowds against a database of known suspects 4 ; scanning licence plates on vehicles 5 ; training for crime scene investigation 6 as well as less-obvious applications (e.g. employing AR for real-time translation 7 with the aim to increase individual functioning of police officers and facilitate a more efficient response to crime.…”

Section: Ai and Ar Technological Landscape For The Security And Safet...mentioning

confidence: 99%

DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions

et al. 2022

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Background: Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains, including the security sector. Several law enforcement agencies (LEAs) employ AI in their daily operations for forensics and surveillance. AR is also gaining traction in security, particularly with the advent of affordable wearable devices. Equipping police officers with the tools to facilitate an elevated situational awareness (SA) in patrolling and tactical scenarios is expected to improve LEAs’ safety and capacity to deliver crucial blows against terrorist and/or criminal threats. Methods: In this paper we present DARLENE, an ecosystem incorporating novel AI techniques for activity recognition and pose estimation tasks, combined with a wearable AR framework for visualization of the inferenced results via dynamic content adaptation according to the wearer’s stress level and operational context. The concept has been validated with end-users through co-creation workshops, while the decision-making mechanism for enhancing LEAs’ SA has been assessed with experts. Regarding computer vision components, preliminary tests of the instance segmentation method for humans’ and objects’ detection have been conducted on a subset of videos from the RWF-2000 dataset for violence detection, which have also been used to test a human pose estimation method that has so far exhibited impressive results, constituting the basis of further developments in DARLENE. Results: Evaluation results highlight that target users are positive towards the adoption of the proposed solution in field operations, and that the SA decision-making mechanism produces highly acceptable outcomes. Evaluation of the computer vision components yielded promising results and identified opportunities for improvement. Conclusions: This work provides the context of the DARLENE ecosystem and presents the DARLENE architecture, analyses its individual technologies, and demonstrates preliminary results, which are positive both in terms of technological achievements and user acceptance of the proposed solution.

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Section: Ai and Ar Technological Landscape For The Security And Safet...mentioning

confidence: 99%

DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions

et al. 2022

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“…Police in other countries uses similar systems, mostly to look for stolen cars [23]. Intriguing initiatives aiming to involve citizens in the detection and analysis of car license plates through interactive games have also been proposed [24].…”

Section: St-openunisthrmentioning

confidence: 99%

System for automatic detection and classification of cars in traffic

Bralić

Musić

2022

St open

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Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video.Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library and Tesseract-based optical character recognition. For the sake of simplicity and speed, the subsystems were run on an embedded Raspberry Pi computer.Results: A camera was mounted on the inside of the windshield to monitor cars in front of the camera. The system processed the camera’s video feed and provided information on the color, license plate, make, and model of the observed car. Knowing the license plate number provides access to details about the car owner, roadworthiness, car or license place reports missing, as well as whether the license plate matches the car. Car details were saved to file and displayed on the screen. The system was tested on real-time images and videos. The accuracies of car detection and car model classification (using 8 classes) in images were 88.5% and 78.5%, respectively. The accuracies of color detection and full license plate recognition were 71.5% and 51.5%, respectively. The system operated at 1 frame per second (1 fps).Conclusion: These results show that running standard machine learning algorithms on low-cost hardware may enable the automatic detection and classification of cars in traffic. However, there is significant room for improvement, primarily in license plate recognition. Accordingly, potential improvements in the future development of the system are proposed.

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“…Hence, the use of AI is needed to be controlled through different regulations (Lauterbach, 2019). In this context, European Union (EU) has already framed GDPR to address the situation (Milaj and Ritsema Van Eck, 2019; Caruana, 2017) with participation and security principles [5][6] GDPR is found applicable when processing of such personal data takes place. As per Article 4(1) of GDPR, personal data is interpreted as such data which is concerned with a natural person who may be identified or identifiable.…”

Section: Review Of Literaturementioning

confidence: 99%

Impact of AI regulation and governance on online personal data sharing: from sociolegal, technology and policy perspective

Chatterjee

Sreenivasulu

2021

JSTPM

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Purpose The purpose of this study is to investigate the impacts of regulations and governance of artificial intelligence (AI) on personal data sharing (PDS) in the context of sociolegal, technology and policy perspective. Design/methodology/approach With the help of theories and literature review, some hypotheses have been formulated and a conceptual model has been developed. These are statistically validated. The validated model has been compared again using impact of regulation and governance of AI as a moderator. The validation has been done using survey by PLS analysis. Findings The study found that there is a high level of positive impact of regulation and governance of AI on the online PDS by the users. Research limitations/implications This study has provided a statistical model which can provide the antecedents of PDS by the online users with the impact of AI regulation and governance as a moderator. The proposed model has explanative power of 92%. Practical implications The study highlighted that there is a necessity of having appropriate AI regulations so that users could share their personal data online without any hesitation. Policymakers and legal fraternity should work together to formulate a comprehensive AI regulation and governance framework. Originality/value To the best of the authors’ knowledge, there is no study on the impact of AI regulation and governance towards PDS and how it impacts on the security, privacy and trust of the online users.

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Capturing licence plates: police-citizen interaction apps from an EU data protection perspective

Cited by 7 publications

References 25 publications

DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions

DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions

System for automatic detection and classification of cars in traffic

Impact of AI regulation and governance on online personal data sharing: from sociolegal, technology and policy perspective

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