AI Technologies Showroom

Project:

Researchers, students and engineers have contributed to develop an AI algorithm capable of locating, identifying and quantifying the species present on footages.

Objectives:

The Mercantour national Park wishes to monitor and identify common and rare species present on its territory from videos extracted from camera traps located on the Roya Valley.

Methodology:

An algorithm composed of convolutional neural networks (Faster-RCNN and InceptionResNetv2) has been implemented thus gaining in efficiency to count and estimate the fauna present.

Context:

The Metropole Nice Côte d’Azur (MNCA) launched an initiative starting in 2021 to study the environmental, social and economical factors influencing the health of its citizen. The 3IA Côte d’Azur was part of this initiative through a collaborative project gathering the MNCA, the University Hospital of Nice, and the economy laboratory of University Côte d’Azur. The mid-term objective of this initiative is to provide geo-localized information helping the guidance and evaluation of public policies. A first use case of this project, aimed at identifying the geographical disparities of breast cancer screening for women aged from 50 to 74 in the department of Alpes-Maritimes. We make this analysis at the scale of the smallest available administrative territory (IRIS) as defined by French national statistics Institute (INSEE), it allows to have enough statistical information at smallest area and avoid working on individuals’ data subject to GDPR.

Data qualification:

A first task of the 3IA Côte d’Azur was to perform data qualification of various open or restricted databases provided by public French institutions (INSEE, IGN, CPAM06, CAF06, MNCA, CHU Nice). Databases were related to 5 major themes (transport, demographics, environment, healthcare, and geography) and they have been audited to detect missing values, outliers and inconsistent data between several databases. After data cleaning, databases were visualized and analysed by the various partners of the project.

Below the map of participation rate of breast cancer screening (either individual or organised screening) for women aged from 50 to 74 from 17/06/2019 to 17/06/2021 (source CPAM06).

Breast cancer

Accessibility to screening centers:

The 3IA Côte d’Azur has also participated in the study of the difficulty to access screening centers through personal vehicles or public transportation at different locations of the department of Alpes-Maritimes. More specifically, the estimation of various accessibility indices to reach certified radiological centers for breast cancer screening was performed as shown the maps below.

Isochrones

Coverage ratio

Motivation:

Person re-identification (ReID) is an illustration of a fundamental problem in Computer Vision, how to represent an object (i.e. its appearance) independently from its perception (i.e. its pose, view-point, illumination and the given sensor). As a core component of intelligent video surveillance system, person ReID has attracted increasing attention in computer vision research community. Given a query person image, a person ReID system seeks the most similar images in the gallery by sorting representation similarity. Although rapid improvements has been witnessed in recent years, person ReID still suffers from real-world environmental factors, such as illumination, pose, view-point and background variance, which degrade the quality of image representations. How to build robust representations that are invariant to the environmental factors with limited data remains a key problem for person ReID. In addition, annotating a large-scale person ReID dataset is a cumbersome task, which strongly limits the scalability of supervised ReID methods.

About the project:

In our work, we first study how to build robust representations for supervised person ReID. Then, we remove the human supervision and design unsupervised algorithms to enhance the flexibility of person ReID.

Source code available here.

Contributors:

Hao Chen, Benoit Lagadec, Francois Bremond.

Demos:

In the demo "Where is Charlie ?" we will show and compare the un-supervised ReID model vs. supervised ReID model over synthetic person dataset (PersonX dataset).

In the demo "Where is my car ?", we compare these models over a dataset composed of cars in real trafic (VeRi dataset).

Motivation:

Standard machine learning approaches require to have a centralized dataset in order to train a model. In certain scenarios like in the biomedical field, this is not straightforward due to several reasons like: privacy concerns, ethical committee approval or transferring of data to a centralized location. This slows down research in healthcare and limits the generalization of certain models.

About the project:

Fed-BioMed is an open source project focused on empowering biomedical research using non-centralized approaches for statistical analysis.

Federated Learning:

Federated Learning (FL) is a machine learning procedure whose goal is to train a model without having data centralized. The goal of FL is to train higher quality models by having access to more data than centralized approaches, as well as to keep data securely decentralized. The main challenges are associated to: communication efficiency, data heterogeneity and security

Demo:

We will show and compare the machine learning procedure of a DenseNet121 neural network model over dataset MedNIST in standalone vs. in Federated Learning.

Fed-BioMed is a project developed by INRIA, in the EPIONE team. Manager: Marco Lorenzi. Source code available here.

Video analytics enables us to measure objectively the behavior of humans by recognizing their everyday activities, their emotion, eating habits and lifestyle. Human behavior can be modeled by learning from a large quantity of data from a variety of sensors to improve and optimize, for instance, the quality of life of people suffering from behavior disorders.

Motivation:

Embedding Artificial Intelligence onto low-power devices is a challenging task that has been partly overcome with recent advances in machine learning and hardware design. Presently, deep neural networks can be deployed on embedded targets to perform different tasks. However, there is still room for optimization of deep neural networks onto embedded devices. These optimizations mainly address power consumption, memory and real-time constraints, but also an easier deployment at the edge. Moreover, there is still a need for a better understanding of what can be achieved for different use cases.

Description:

This project focuses on quantization and deployment of deep neural networks onto low-power 32-bit microcontrollers. A new framework for end-to-end deep neural networks training, quantization and deployment is proposed. This framework, called MicroAI, is designed as an alternative to existing inference engines. Our framework can indeed be easily adjusted and/or extended for specific use cases.

We specifically demonstrate the efficiency of our framework in the case of Human Activity Recognition with the connected glasses from Ellcie-Healthy company. The prediction is made on-board based on IMU data, preserving privacy and energy concumption. A specific dataset has been created in colllaboration with Ellcie-Healthy, named UCA-EHAR. Thanks to MicroAI, the embedded neural network only needs 200 kB of flash and 130 kB of RAM and can reach 24 hours of autonomy with its rechargeable battery.

Contributors:

Pierre-Emmanuel Novac (PhD student, UCA), Dr. Alain Pegatoquet (UCA), Pr. Benoît Miramond (UCA, 3IA), Dr. Christophe Caquineau (Ellcie-Healthy)

Developed by expert.ai and the University of Siena, WebCrow is the first AI-based software that tackles a language game using NLP technology and the richest self-updating repository of human knowledge available: the web. Designed to be multilingual, WebCrow is the world’s first Italian and English crossword puzzle solver.