Project description
Teaching cars to drive like humans
From connected vehicles to self-driving cars, driverless vehicles are set to become a game changer in coming years. Traditional automobile manufacturers are racing to develop fully autonomous vehicles to meet (future) demand. Paving the way for this transformation is the advance of autonomous vision algorithms. The EU-funded VUAD project will develop a deep learning method for multi-object tracking on graph-structured data. The project will also extend this to joint video object detection and tracking by exploiting temporal cues, in order to improve both detection and tracking performance. In addition, VUAD will propose a background motion model for the static parts of the scene in an unsupervised manner. The goal is to combine the proposed algorithms into a unified video-understanding module.
Objective
Autonomous vision aims to solve computer vision problems related to autonomous driving. Autonomous vision algorithms achieve impressive results on a single image for various tasks such as object detection and semantic segmentation, however, this success has not been fully extended to video sequences yet. In computer vision, it is commonly acknowledged that video understanding falls years behind single image. This is mainly due to two reasons: processing power required for reasoning across multiple frames and the difficulty of obtaining ground truth for every frame in a sequence, especially for pixel-level tasks such as motion estimation. Based on these observations, there are two likely directions to boost the performance of tasks related to video understanding in autonomous vision: unsupervised learning and object-level reasoning as opposed to pixel-level reasoning. Following these directions, we propose to tackle three relevant problems in video understanding. First, we propose a deep learning method for multi-object tracking on graph structured data. Second, we extend it to joint video object detection and tracking by exploiting temporal cues in order to improve both detection and tracking performance. Third, we propose to learn a background motion model for the static parts of the scene in an unsupervised manner. Our long-term goal is also to be able to learn detection and tracking in an unsupervised manner. Once we achieve these stepping stones, we plan to combine the proposed algorithms into a unified video understanding module and test its performance in comparison to static counterparts as well as the state-of-the-art algorithms in video understanding.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning unsupervised learning
- natural sciences computer and information sciences artificial intelligence computer vision object detection
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF-EF-ST - Standard EF
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2019
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
34450 Istanbul
Türkiye
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.