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Analysis and Representation of Complex Activities in Videos

The goal of the project is to automatically analyse human activities observed in videos. Any solution to this problem will allow the development of novel applications. It could be used to create short videos that summarize daily a... ver más

31/05/2021

RHEINISCHE FRIEDRI...

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

RHEINISCHE FRIEDRICHWILHELMSUNIVERSITAT BONN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-05-31

ERC-StG-2015: ERC Starting Grant

I+D

Cerrada hace 10 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

RHEINISCHE FRIEDRICHWILHELMS...

1.50M€ | Lider

SOLUCIONES INFORMATICAS AMBI...

189.47K€ | Participante

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Duración del proyecto: 62 meses Fecha Inicio: 2016-03-01
Fecha Fin: 2021-05-31

Líder del proyecto

RHEINISCHE FRIEDRICHWILHELMSUNIVERSITAT BONN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The goal of the project is to automatically analyse human activities observed in videos. Any solution to this problem will allow the development of novel applications. It could be used to create short videos that summarize daily activities to support patients suffering from Alzheimer's disease. It could also be used for education, e.g., by providing a video analysis for a trainee in the hospital that shows if the tasks have been correctly executed. The analysis of complex activities in videos, however, is very challenging since activities vary in temporal duration between minutes and hours, involve interactions with several objects that change their appearance and shape, e.g., food during cooking, and are composed of many sub-activities, which can happen at the same time or in various orders. While the majority of recent works in action recognition focuses on developing better feature encoding techniques for classifying sub-activities in short video clips of a few seconds, this project moves forward and aims to develop a higher level representation of complex activities to overcome the limitations of current approaches. This includes the handling of large time variations and the ability to recognize and locate complex activities in videos. To this end, we aim to develop a unified model that provides detailed information about the activities and sub-activities in terms of time and spatial location, as well as involved pose motion, objects and their transformations. Another aspect of the project is to learn a representation from videos that is not tied to a specific source of videos or limited to a specific application. Instead we aim to learn a representation that is invariant to a perspective change, e.g., from a third-person perspective to an egocentric perspective, and can be applied to various modalities like videos or depth data without the need of collecting massive training data for all modalities. In other words, we aim to learn the essence of activities.

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