Negative Filtering of CCTV Content -Forensic Video Analysis Framework

Franck Jeveme Panta (1) , André Péninou (1, 2) , Florence Sèdes (1)

1 IRIT-SIG - Systèmes d’Informations Généralisées
2 UT2J - Université Toulouse - Jean Jaurès

This paper presents our work on forensic video analysis that aimed to assist videosurveillance operators by reducing the volume of video to analyze during the search for post-evidence in videos. This work is conducted in collaboration with the French National Police and is based on requirements defined in a project related to videos analysis in the context of investigations. Due to the constant increasing volume of video generated by CCTV cameras, one of the investigators' goals is to reduce video analysis time. For this purpose, we propose a negative filtering approach based on quality and usability/utility metadata, enabling to eliminate video sequences that do not satisfy requirements for their analysis through automatic processing. Our approach involves a data model which is able to integrate different levels of video metadata, and an associated query mechanism. Experiments performed using the developed framework demonstrate the utility of our approach in a real-world case. Results show that our approach helps CCTV operators to significantly reduce video analysis times.

Keywords: metadata; CCTV Systems; forensic video analysis; video evidence

Domains: Databases [cs.DB]; Social and Information Networks [cs.SI]; Artificial Intelligence [cs.AI]; Cryptography and Security [cs.CR]

Franck Jeveme Panta, André Péninou, Florence Sèdes. Negative Filtering of CCTV Content -Forensic Video Analysis Framework. 15th Conference on Availability, Reliability and Security (ARES 2020), Aug 2020, Dublin, Ireland. pp.1-10, ⟨10.1145/3407023.3407069⟩. ⟨hal-03001008⟩