Rémi Auguste

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Our goal is to automatically identify faces in TV content without pre-defined dictionary of identities. Most of methods are based on identity detection (from OCR and ASR) and require a propagation strategy based on visual clusterings. In TV content, people appear with many variation making the clustering very difficult. In this case, identifying speakers(More)
The goal of the PERCOL project is to participate to the REPERE multimodal challenge by building a consortium combining different scientific fields (audio, text and video) in order to perform person recognition in video documents. The two main scientific issues addressed by the challenge are firstly multimodal fusion algorithms for automatic person(More)
This paper describes a multi-modal person recognition system for video broadcast developed for participating in the DefiRepere challenge. The main track of this challenge targets the identification of all persons occurring in a video either in the audio modality (speakers) or the image modality (faces). This system is developed by the PERCOL team involving(More)
Our goal is to automatically identify faces in TV broadcast without a pre-defined dictionary of identities. Most methods are based on identity detection (from OCR and ASR) and require a propagation strategy based on visual clustering. In TV content, people appear with many variations making the clustering difficult. In this case, speaker clustering can be a(More)
The annotation of video streams by automatic content analysis is a growing field of research. The possibility of recognising persons appearing in TV shows allows to automatically structure ever-growing video archives. We propose a new descriptor to re-identify persons featured in videos, that is to say, to spot all occurrences of persons throughout a video.(More)
This paper introduces a novel person track dataset dedicated to person re-identification. The dataset is built from a set of real life TV shows broadcasted from BFMTV and LCP TV French channels, provided during the REPERE challenge. It contains a total of 4,604 persontracks (short video sequences featuring an individual with no background) from 266 persons.(More)
The analysis and interpretation of video contents is an important component of modern vision applications such as surveillance, motion synthesis and web-based user interfaces. A requirement shared by these very different applications is the ability to learn statistical models of appearance and motion from a collection of videos, and then use them for(More)