Post-Doctoral Research Visit F/M Designing a foundation model for unified video representation learning for multiple camera conditions such as CCTV, dash cam, UAV cam, movies camera, etc...

The Inria center at Université Côte d'Azur includes 42 research teams and 9 support services. The center’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regional economic players.

With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur  is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.

Inria, the French National Institute for Computer Science and Applied Mathematics, promotes “scientific excellence for technology transfer and society”. Graduates from the world’s top universities, Inria's 2,700 employees rise to the challenges of digital sciences. With its open, agile model, Inria can explore original approaches with its partners in industry and academia and provide an efficient response to the multidisciplinary and application challenges of digital transformation. Inria is the source of many innovations that add value and create jobs.

Team

The STARS research team combines advanced theory with cutting-edge practice focusing on cognitive vision systems.

Team web site : https://team.inria.fr/stars/

Scientific context

STARS group works on automatic video monitoring and human behavior understanding for health applications. The Deep Learning platform developed in STARS, detects mobile objects, tracks their trajectory, and recognizes related behaviors predefined by experts. This platform contains several techniques for detecting people and for recognizing human postures/gestures using conventional cameras. However, there are scientific challenges in people tracking when dealing with real-world scenes: cluttered scenes, handling wrong and incomplete person segmentation, handling static and dynamic occlusions, low contrasted objects, moving contextual objects (e.g. chairs), similar appearance of clothes among different people ...

This Project aims to detect critical situations in the daily life of elderly people living at home alone. We believe that a system that can detect potentially dangerous situations will give peace of mind to frail older people as well as to their caregivers. This will require not only recognition of ADLs but also an evaluation of the way and timing in which they are being carried out.

The users we are targeting should be in relatively good health condition. We do not want to address serious dementia problems but intend to give support to people who suffer from age-related forgetfulness (in many cases the start of an Alzheimer's condition). The users should live in their own house by themselves and generally be able to carry out their day-to-day activities. The system we want to develop is intended to help them and their relatives feel more comfortable because they know potentially dangerous situations will be detected and reported to caregivers if necessary.

Video data is generated by a wide range of camera types, each with unique characteristics and applications. From static surveillance cameras monitoring urban spaces to UAV (Unmanned Aerial Vehicle) cameras capturing aerial perspectives and cinematic cameras producing dynamic scenes, the variety of video data sources presents a unique challenge for video analysis models. Traditional approaches, often specialised for a single camera type, struggle to generalise across different sources. This limitation reduces the efficiency and adaptability of video analytics systems, particularly in real-world applications where footage from multiple camera types needs to be processed cohesively. This proposal seeks to develop a Unified Video Representation Learning (UVRL) framework capable of generating robust and transferable video representations for multiple camera types, including static, moving, UAV, and cinematic cameras. By designing a flexible model architecture that adapts to the distinct characteristics of each camera type, this project aims to create a versatile framework capable of delivering high-quality, context-aware representations across diverse video sources.

The Inria STARS team is seeking an engineer with a strong background in computer vision, deep learning, and machine learning.
The candidate is expected to conduct research related to the development of computer vision algorithms for video understanding.

Main activities:

• Analyze the requirements of doctors and patients/end-users and Study the limitations of existing solutions.
• Propose a new algorithm for detecting the behaviors of patients/end-users
• Evaluate and optimize the proposed algorithm on the targeted video datasets
• Oral presentation and Write reports
• Submit a scientific paper to a conference

Candidates must hold a Master's degree or equivalent in Computer Science or a closely related discipline by the start date.

The candidate must be grounded in computer vision basics and have solid mathematical and programming skills.

With theoretical knowledge in Computer Vision, OpenCV, Mathematics, Deep Learning (PyTorch, TensorFlow), and technical background in C++ and Python programming, and Linux.

The candidate must be committed to scientific research and substantial publications.

In order to protect its scientific and technological assets, Inria is a restricted-access establishment. Consequently, it follows special regulations for welcoming any person who wishes to work with the institute. The final acceptance of each candidate thus depends on applying this security and defense procedure.

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Contribution to mutual insurance (subject to conditions)

Gross Salary : 2788 € per month