Automated Detection of SaliencieS from Operators' Point of View and Intelligent Compression of DronE videos – DISSOCIE
The aerial surveillance, monitoring and observation with drone present major challenges in terms of defense, 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 modeling, 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 modeling 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 rewatch 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 modeling, salient object detection and segmentation, human observer modeling, 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 modeling 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 with a TRL-4 target. The expected impacts of the project include a dissemination action to the scientific community (scientific papers) and a real interest for the drone groups of the defense: bringing comfort to military observation and reduce human error; enhancing the effectiveness of military operations through better saliency detection; enabling better management of the human potential of the armed forces, which is heavily used during periods facing growing terrorist threat and in the current States of Emergency in France.
Madame Lu ZHANG (Institut d'électronique et de télécommunications de Rennes)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
IRISA Institut de recherche en informatique et systèmes aléatoires
LS2N Laboratoire des Sciences du Numérique de Nantes
IETR Institut d'électronique et de télécommunications de Rennes
Help of the ANR 298,809 euros
Beginning and duration of the scientific project: December 2017 - 36 Months