NDLI: Space-time Histograms And Their Application To Person Re-identification In TV Shows

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Space-time Histograms And Their Application To Person Re-identification In TV Shows

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Space-time Histograms And Their Application To Person Re-identification In TV Shows

Content Provider	ACM Digital Library
Author	Tirilly, Pierre Auguste, Rémi Martinet, Jean
Abstract	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. Our approach is dynamic as it benefits from motion information contained in videos, whereas the static approaches are solely based on still images. We extract person-tracks from videos and match them using a new descriptor and its associated similarity measure: the space-time histogram. The originality of our approach is the integration of temporal data into the descriptor. Experiments show that it provides a better estimation of the similarity between person-tracks. Our contribution has been evaluated using a corpus of real life french TV shows broadcasted on BFMTV and LCP TV channels and on some annotated episodes from "Buffy: the Vampire Slayer". Experimental results show that our approach significantly improves the precision of the re-identification process thanks to the use of the temporal dimension.
Starting Page	91
Ending Page	97
Page Count	7
File Format	PDF
ISBN	9781450332743
DOI	10.1145/2671188.2749332
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Video Spatial-temporal descriptor Person re-identification
Content Type	Text
Resource Type	Article

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