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9 juillet 2018

PostDoc: Computational models of visual attention aided by geo-localized and time stamp information

Catégorie : Post-doctorant

Position type: Postdoc

Functional area: Rennes

Team: http://www-percept.irisa.fr/

Scientific advisors: olemeur@irisa.fr

Duration: 12 months

Start date: possibly from September 2018

Net salary: around 2200 euros per month



DISSOCIE stands for “Détection automatIque des SaillanceS du point de vue des Opérateurs et Compression Intelligente des vidéos de dronEs“ or “Automated Detection of SaliencieS from Operators' Point of View and Intelligent Compression of DronE videos”.

Home page : https://sites.google.com/insa-rennes.fr/projetanrastrid-dissocie

The aerial surveillance, monitoring and observation with drone present major challenges in terms of defence, security and environment. For example, France and Britain have agreed to invest 2 billion euros in a project to build next-generation multi-role drones capable of carrying out surveillance and observation missions, identifying targets and launching strikes on enemy territory for future operational capacity beyond 2030. However, the observation, targets identification and surveillance missions are currently being carried out by human operators, who do not have the ability to fully and effectively exploit all available drone videos. The science and the technology of the eye-tracking study, visual attention modelling, human operator models, and intelligent compression opens up new possibilities to meet these challenges.

In this context, the DISSOCIE project aims to develop automatic and semi-automatic operator models capable of detecting salient areas from the point of view of human operators, by considering the low-level characteristics of the salient content in the videos, geo-temporally localized contextual information, and the expertise and the detection strategies of human operators. Machine learning can be used at different levels of this modelling process. The new HEVC video compression standard and the scalable coding will also be exploited in this project to improve the efficiency when the experts re-watch the videos. The originality of the project lies in an innovative approach to jointly address these challenges based on the complementarity and the strengthening of the scientific expertise gathered in the consortium: especially on eye-tracking analysis, visual fixation prediction, visual attention modelling, salient object detection and segmentation, human observer modelling, and video compression. The project is broken down into 4 tasks: Construction of a ground truth (T1 Task), Development of models and algorithms of geo-temporally localized saliency (T2 Task), Human operator modelling via machine learning and its integration with the geo-temporally localized saliency (Task T3), Intelligent compression based on salient regions and metadata insertion (T4 Task). The DISSOCIE initiative, from its consortium formed by three academic members (IETR/VADDER, IRISA/PERCEPT, LS2N/IPI), will implement an applied research program.


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