The ADDPRIV Project
The ADDPRIV project (Automatic Data relevancy Discrimination for a PRIVacy-sensitive video surveillance) seeks to improve public safety by ensuring the individuals' privacy right, enriching the current video surveillance systems through an automatic discrimination of relevant data recorded.
The project addresses the challenge of determining through an automatic, accurate and reliable manner which information obtained from a distributed system of surveillance cameras is relevant from the security perspective and which is not, and can be safely deleted. This will limit unnecessary data storage and will protect the citizens’ privacy right.
The main goals of the ADDPRIV project are:
- ADDPRIV proposes novel knowledge and developments to limit the storage of unnecessary data throughout existing multicamera networks in order for them to better comply with citizen's privacy rights.
- ADDPRIV addresses the challenge of determining in a precise and reliable manner private data captured by video surveillance systems that are not relevant from a security perspective.
- ADDPRIV proposes solutions for automatic discrimination of relevant data recorded on a multicamera network, related to an individual whose suspicious behavior triggered an alert. Relevant data not only corresponds to video scenes capturing individuals' 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.
1. General Information
2. Current Situation
3. Future Needs
4. ADDPRIV output
2. Current Situation
- Technologies that can guarantee a well-balanced trade off between security and human rights (equality, freedom and privacy) in the use of video surveillance in public places need to be developed and are committed to have a major impact on the European societal acceptance of video surveillance.
- Current video surveillance systems fail to be privacy sensitive.
- Little research has been done in order to minimize the impact of video surveillance on privacy.
- A number of relevant techniques are under development, based on tagging of authorized individuals by means of face recognition technologies and RFID, but are not suitable for video surveillance in public places.
3. Future needs
- Development of algorithms for trustworthy Data Relevancy Discrimination, implementing correlation patterns on existing multicamara network.
- Development of secure erase techniques affecting only specified files on solid state disc, keeping fully reliable and accessible the rest of the information on that storage media.
- Development of solutions that can be easily and rapidly adaptable to different scenarios (different infrastructures as well as different legislations).
- Development of knowledge on the rationale use of these video surveillance systems that define how and when they are to be used.
4. ADDPRIV Output
- Data relevancy discrimination obtained by means of automatic analysis tools.
- Information indexing according to relevancy and storage with different levels and appropriate management restrictions.
- Faster browsing time avoiding the need for agents of viewing non relevant information and the subsequent privacy infringement.
- Secure erase of the irrelevant data.
- Contribution to a well-balanced trade off between security and privacy in the use of video surveillance in public places bringing a significant impact on the European societal acceptance of video surveillance systems.
