ADDPRIV

Automatic Data relevancy Discrimination for a PRIVacy-sensitive video surveillance

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 Obiettivo del progetto (Objective)

'ADDPRIV proposes novel knowledge and developments to limit the storage of unnecessary data, to be implemented on existing multicamera networks in order to make them better comply with citizen´s privacy rights.

ADDPRIV tackles the challenge of determining in a precise and reliable manner private data from video surveillance which is not relevant from the perspective of security and which need not to be stored. ADDPRIV proposes solutions for automatic discrimination of relevant data recorded on a multicamera network, related to an individual whose suspicious behaviour triggered an alert. Relevant data not only corresponds to video scenes capturing individuals´s suspicious behavior (smart video surveillance), but also automatically extracting images on these individuals recorded before and after the suspicious event and across the surveillance network.

Algorithms for automatic browsing, identification and retrieval of images on the suspicious individuals across the multicamera network will be developed, implemented and validated in a real life scenario. These solutions will be able to discriminate data relevancy and index relevant images, setting different storage levels with different data access management. Solutions for secure deletion of non-relevant video files will also be developed, according to the highest security standards. ADDPRIV will therefore provide required tools for limiting the visualization and storage of unnecessary data, corresponding to law abiding citizens.

ADDPRIV solutions´ validation will be guided by criteria and metrics determined by social and ethical experts and end users, establishing the characteristics that a surveillance system must fulfill for effectiveness and integrity in citizens privacy protection. ADDPRIV will analyse the impact of our system on citizen´s human rights and end users organizational processes, aiming to contribute towards establishing a standard on how and when these systems are to be used.'

Introduzione (Teaser)

Balancing security and privacy in video surveillance poses several challenges. An EU-funded initiative developed a novel video surveillance system to automatically, accurately and reliably determine relevant information and securely erase unimportant data.

Descrizione progetto (Article)

The 'Automatic data relevancy discrimination for a privacy-sensitive video surveillance' (http://www.addpriv.eu/ (ADDPRIV)) project addressed the demanding task of balancing security and privacy in video surveillance. Overall, the aim was to develop new knowledge and algorithms to build on smart video surveillance solutions for greater privacy compliance.

Project members first defined the requirements for better conformity with privacy rights. An end-user and ethics advisory board was established and a preliminary scoreboard produced for evaluating ADDPRIV's system compliance with such rights. Following this, and based on reviews and discussion of current systems, the system was designed.

A system architecture was defined and solutions outlined for each of the system blocks. Data relevancy discrimination algorithms were created that automatically generate alerts. Additional functionalities were designed for these algorithms to adjust to different scenarios. Intelligent storage and secure deletion technologies were also developed, including an algorithm for data labelling and sorting based on the relevancy of images. The developed solutions were then implemented and validated in real-life scenarios.

Lastly, a series of reports was produced examining the impact of the proposed solutions. These include an analysis of the human rights, civil liberty and public participation potential of the solutions, a study on when and how to use them, and an assessment of the possible effects of the technology on end-user organisations.

ADDPRIV introduced cutting-edge video surveillance technology that respects a citizen's right to privacy. The project enhances the effectiveness and efficiency of video surveillance processes and reduces reaction time in critical situations. It also has the potential to improve social acceptance of video surveillance technologies.