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Multi-Target Multi-Camera tracking (MTMC)
Stage, Stains (France)
Publiée le 21/05/2020 , modifiée le 21/05/2020
Débutant (-3 ans)
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Descriptif de la société

ENGIE is a global player in the energy sector, resolutely committed to the energy transition and expert in 3business lines: electricity, natural gas and energy services. ENGIE has 153,090 employees in more than 50 countries for a 2016 turnover of66.6 billion euros. In search of innovative and motivated professionals to embody the future of energy at the service of its customers, ENGIErecruits thousands of talents around the world. Interns will join a world of fulfilling and innovative work, promoting agility and creativity to meetthe energy challenges of today and the future. ENGIE Lab CRIGEN is the center of research, development and operational expertisededicated to gas, new energies, and new technologies. Located in the Paris region in the city of Stains (93240), it has 200 employees. Itprovides tested, proven and marketable industrial applications, and is committed to sharing novel ideas, scientific knowledge, and technicalexpertise. Its ability to innovate is a key advantage for the ENGIE Group.

Descriptif du poste

Multi-Target Multi-Camera tracking consists of detecting, identifying and tracking objects in multiple videos. Generally, this kind of algorithm isbased on different modules as a detector, single-camera tracking, pixel to GPS and re-identification (RE-ID).Multi-Target Multi-Camera tracking is used for intelligent transportation systems (ITS).The subject of this internship is to review and develop a Multi-Target Multi-Camera tracking based in the modules already developed in ourlaboratory.Methodology:Do a review of the state of the art.Experiment and implement the different techniques proposed in the literature.Propose and implement a different solution.Test and compare the algorithms.Publish and divulge the results

Profil recherché

Knowledge of deep learning techniques applied to computer vision: deep convolutional networks, autoencoders, image (pre)processing,regularizationUnderstanding of standard computer vision techniques: filtering, transformations, descriptors and detectorsKnowledge and understanding of the mathematics underlying all of the above: probability and statistics, optimization, linear algebra, numericalcomputationStrong Python skillsFamiliarity with one of the Deep Learning frameworks and tools like Tensorflow, Keras, PyTorch.Disciplined self-starter, able to be highly productive both working alone and within an agile development teamExperience with the UNIX environmentFluent in English

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Il est rappelé, conformément à l'article 27 de la loi 78/17 du 6 janvier 1978 et au règlement (UE) 2016/679 du Parlement européen et du Conseil du 27 avril 2016 que :
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