Modeling metadata of CCTV systems and Indoor Location Sensors for automatic filtering of relevant video content

Franck Jeveme Panta (1, 2) , Geoffrey Roman-Jimenez (1, 2) , Florence Sèdes (1, 2)

1 IRIT-SIG - Systèmes d’Informations Généralisées
2 UT3 - Université Toulouse III - Paul Sabatier

Location sensors and Closed-circuit Television(CCTV) cameras are widely used for the surveillance of people,objects and areas. These devices (sensors, CCTV cameras, etc.)generate a large amount of heterogeneous data, making theiranalysis and management difficult and very time-consuming. Inthe context of video-surveillance, automatic extraction of therelevant information among the mass of multi-sources informa-tion produced by these systems could significantly reduce theinvestigation time and facilitate their analysis. In this paper, wepropose an approach that combines data from Indoor LocationSensors and metadata from CCTV cameras to automaticallyretrieve relevant video segments during research of evidenceaccident or crime events in indoor environments. The proposedmethod consists in i) the reconstruction of the mobile devicetrajectories from indoor location sensors and ii) the identificationof the CCTV cameras intersecting the reconstructed trajectories.To ensure industrial transferability, indoor location sensors datawere embedded in a generic model of CCTV cameras metadatathat instantiates the standard ISO 22311 (relative to digital video-surveillance contents). We provide an experimental evaluationdemonstrating the utility of our approach in a real-world case.Results show that our method helps the CCTV operators toeffectively retrieve the relevant video and drastically reduce thetime of analysis.

Mots clés: Spatio-temporal; Trajectories; Tracking; CCTV System; Metadata

Domaines: Base de données [cs.DB]

Franck Jeveme Panta, Geoffrey Roman-Jimenez, Florence Sèdes. Modeling metadata of CCTV systems and Indoor Location Sensors for automatic filtering of relevant video content. 12th IEEE International Conference on Research Challenges in Information Science (RCIS 2018), May 2018, Nantes, France. pp.1-9, ⟨10.1109/RCIS.2018.8406677⟩. ⟨hal-02280353⟩