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AddPriv Automatic Data Relevancy Discrimination for a PRIVacy-sensitive Video Surveillence

• Project Team:
  • Project Leader: Prof. Margaret O’Mahony
  • Research Student: Orla McCarthy
• Description:
  • 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.