Space-time Histograms And Their Application To Person Re-identification In TV Shows
Rémi Auguste, Jean Martinet, Pierre Tirilly · 2015
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.