Joint Crypto-Blockchain Scheme for Trust-Enabled CCTV Videos Sharing

Tahir, Mehwish; Asghar, Mamoona Naveed; Kanwal, Nadia; Lee, Brian; Qiao, Yuanhua

doi:10.1109/blockchain53845.2021.00054

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…• Undoubtedly, AI simplifies the lives of people in smart cities, but it poses certain complications, including data collection and protection when shared in infrastructure. Event-based privacy protection using encryption [3], [44], [45] in the event-based summarized videos along with the key management schemes is another extension of the proposed system.…”

Section: Future Directionsmentioning

confidence: 99%

“…The surveillance systems installed in smart vehicles and city infrastructure are the foundation of ITS that can provide brisk services to the residents in case of any calamity. However, considering surveillance systems also brings into focus the privacy of individuals and associated objects, particularly due to the European General Data Protection Regulation (EU-GDPR) [2], [3]. EU-GDPR high-lights two concerns: using individuals' personal/associated data without consent and collecting minimal data for providing services.…”

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confidence: 99%

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Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

Tahir,

Qiao,

Kanwal

et al. 2023

IEEE Access

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Closed-circuit television (CCTV) systems have become pivotal tools in modern urban surveillance and traffic management, contributing significantly to road safety and security. This paper introduces an effective solution that capitalizes on CCTV video analytics and an event-driven framework to provide realtime updates on road traffic events, enhancing road safety. Furthermore, this system minimizes the storage requirements for visual data while retaining crucial details related to road traffic events. To achieve this, a two-step approach is employed: (1) training a Deep Convolutional Neural Network (DCNN) model using synthetic data for the classification of road traffic (accident) events and (2) generating video summaries for the classified events. Privacy laws make it challenging to obtain extensive real-world traffic data from open-source datasets, and this challenge is addressed by creating a customised synthetic visual dataset for training. The evaluation of the synthetically trained DCNN model is conducted on ten real-time videos under varying environmental conditions, yielding an average accuracy of 82.3% for accident classification (ranging from 56.7% to 100%). The test video related to the night scene had the lowest accuracy at 56.7% because there was a lack of synthetic data for night scenes. Furthermore, five experimental videos were summarized through the proposed system, resulting in a notable 23.1% reduction in the duration of the original fulllength videos. Overall, this proposed system holds significant promise for event-based training of intelligent vehicles in Intelligent Transport Systems (ITS), facilitating rapid responses to road traffic incidents and the development of advanced context-aware systems.

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Section: Future Directionsmentioning

confidence: 99%

mentioning

confidence: 99%

Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

Tahir,

Qiao,

Kanwal

et al. 2023

IEEE Access

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“…Finally, the summarized data needs to be protected before storing them on a device. EU-GDPR recommends only encryption as a reversible data protection safeguard [15,16]. In order to ensure GDPR compliance, we have implemented encryption to ensure that summarized videos cannot be accessed by unauthorised stakeholders.…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities

et al. 2023

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The purpose of smart surveillance systems for automatic detection of road traffic accidents is to quickly respond to minimize human and financial losses in smart cities. However, along with the self-evident benefits of surveillance applications, privacy protection remains crucial under any circumstances. Hence, to ensure the privacy of sensitive data, European General Data Protection Regulation (EU-GDPR) has come into force. EU-GDPR suggests data minimisation and data protection by design for data collection and storage. Therefore, for a privacy-aware surveillance system, this paper targets the identification of two areas of concern: (1) detection of road traffic events (accidents), and (2) privacy preserved video summarization for the detected events in the surveillance videos. The focus of this research is to categorise the traffic events for summarization of the video content, therefore, a state-of-the-art object detection algorithm, i.e., You Only Look Once (YOLOv5), has been employed. YOLOv5 is trained using a customised synthetic dataset of 600 annotated accident and non-accident video frames. Privacy preservation is achieved in two steps, firstly, a synthetic dataset is used for training and validation purposes, while, testing is performed on real-time data with an accuracy from 55% to 85%. Secondly, the real-time summarized videos (reduced video duration to 42.97% on average) are extracted and stored in an encrypted format to avoid un-trusted access to sensitive event-based data. Fernet, a symmetric encryption algorithm is applied to the summarized videos along with Diffie–Hellman (DH) key exchange algorithm and SHA256 hash algorithm. The encryption key is deleted immediately after the encryption process, and the decryption key is generated at the system of authorised stakeholders, which prevents the key from a man-in-the-middle (MITM) attack.

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Integrating industry 4.0 technologies for the administration of courts and justice dispensation—a systematic review

Bhatt,

Bahuguna,

Swami

et al. 2024

Humanit Soc Sci Commun

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The judiciary is a foundation of democracy, upholding the rule of law and protecting rights. Efficient court administration is vital for public trust, justice, and timely proceedings. Currently, courts face challenges such as inconsistencies, adjournments, and absence of transparency, undermining the justice system. Traditional manual processes and paper-based documentation cause significant backlogs, slow resolutions, and limited public access. As case volumes and complexities rise, modernizing court administration through digital transformation is progressively critical. Currently, many countries are integrating technologies in the courts for its administration and other activities. In recent years, courts and judges have been subjected to pressure to improve performance, uplifting judicial effectiveness to a top priority. Subsequently, several countries have integrated simplification and digitization strategies in judicial initiatives to enhance court efficiency. Switzerland’s Justitia 4.0 and Brazil’s PJE are notable initiatives that focused to strengthen court administration through digitalization. These aspects motivated this study to examine the detailed integration of industry 4.0 technologies such as the Internet of things, cloud computing, blockchain, big data analytics, robotics, drones, Metaverse, digital twins, and artificial intelligence for court administration with digitalized infrastructure. According to the study, integrating these technologies in less complex cases helps minimize expenditures and save time, making to resolve cases conveniently, efficiently, and superiorly. The study also identified the challenges and issues associated with industry 4.0 technologies such as evidence gathering, evidence preservation, robot judges for pre-judgment analysis, and judgment delivery, which future studies need to be explored.

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Joint Crypto-Blockchain Scheme for Trust-Enabled CCTV Videos Sharing

Cited by 5 publications

References 15 publications

Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities

Integrating industry 4.0 technologies for the administration of courts and justice dispensation—a systematic review

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