Introducing FoxPersonTracks: A benchmark for person re-identification from TV broadcast shows

Abstract

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. The dataset has been built from the REPERE dataset by following several automated processing and manual selection/filtering steps. It is meant to serve as a benchmark in person re-identification from images/videos. The dataset also provides re-identifications results using space-time histograms as a baseline, together with an evaluation tool in order to ease the comparison to other re-identification methods.