Phd Position F/m

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat **:CDD

**Niveau de diplôme exigé **:Bac + 5 ou équivalent

**Fonction **:Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Inria Sophia Antipolis - Méditerranée center counts 34 research teams as well as 7 support departments. The center's staff (about 500 people including 320 Inria employees) is made up of scientists of different nationalities (250 foreigners of 50 nationalities), engineers, technicians and administrative staff. 1/3 of the staff are civil servants, the others are contractual agents. The majority of the center’s research teams are located in Sophia Antipolis and Nice in the Alpes-Maritimes. Four teams are based in Montpellier and two teams are hosted in Bologna in Italy and Athens. The Center is a founding member of Université Côte d'Azur and partner of the I-site MUSE supported by the University of Montpellier.

**Contexte et atouts du poste**:
**Team**

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

Scientific context

On top of the aforementioned issue of separated training, short-term tracking and long-term tracking have the same objective (data association) but they have different inputs. Short-term tracking deals with per frame feature representation of an object and long-term tracking needs to deal with a historic feature representation that encapsulates the myriad of changes of an object across a larger frame span. In other words, we need a memory that tracks said changes, that is differentiable and can back-propagate the information all the way up to the detection task.

**Mission confiée**:
This work consists in designing efficient long-term People Monitoring for instance by Joint Detection and Tracking algorithms. One potential approach could use differentiable Memory Banks to build a Deep Learning memory-based architecture that can be trained to learn a feature representation of a tracklet. Therefore, the main difference with respect to the current state-of-the-art is that this MemoryTracker will be conceived to mitigate the loss of information from training separately both detection, short term tracking and long-term tracking tasks. Designing an efficient memory-based architecture is far from evident. Indeed, the first challenge is to be able to infer dense representations (i.e. tracklet vectors). To do so, we propose the use of ROI-alignment from the pipeline of deformable DETR detector. We also can take advantage of joint detection and short-term tracking by using 3D CNNs, this can allow us to have temporal and spatial information that is not available with vanilla 2D CNNs. The use of 3DCNNs can output more reliable tracklets over a small number of frames and use that information to better update the MemoryBank.

In addition to allowing a truly end-to-end pipeline, the MemoryTracker could overcome the batch training problem by storing the tracklet feature vector with an intra-batch loss and an out-of-batch loss. Both losses could be based on triplet loss functions that depend on the current input sequence (intra batch) and the following sequences (out-of-batch). However, while the features of the current frames are given to the detection pipeline, the features of the previous frames are given to the MemoryBank.

Schedule:
1st year:

- Study the limitations of existing DL People Tracking algorithms.
- Proposing a new approach for People Tracking using Joint Detection and Tracking.

2nd year:

- Start to Improve the proposed DL People Tracking approach.
- Writing papers

3rd year:

- Evaluate, improve and optimize proposed DL People Tracking approach.
- Writing papers and PhD manuscript.

**Principales activités**:
The Inria STARS team is seeking for a Ph.D. researcher with strong background in computer vision, deep learning and machine learning.

**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 proposed algorithm on the targeted video datasets
- Oral presentation and Write reports
- Submit a scientific paper to a conference

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

**Avantages**:

- 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 (after 6 months of employment) and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocationa