TY - RPRT AB - In this report, we show that facial descriptors can be used very effectively in conjunction with a tracklet-based multi-person tracker both to localize and to identify or re-identify people over long sequences. Thus, we can reliably deliver both trajectories and identities in crowded scenes. Furthermore, the whole approach is fast enough to be implemented in real-time. Our key insight is that this can be done even though the faces can only be recognized relatively infrequently. T1 - Facial Descriptors for Identity-Preserving Multiple People Tracking DA - 2013 AU - Zervos, Michail AU - BenShitrit, Horesh AU - Fleuret, François AU - Fua, Pascal PP - Lausanne ID - 187534 KW - People Tracking KW - Facial Recognition KW - Identification KW - Re-Identification UR - http://infoscience.epfl.ch/record/187534/files/top_1.pdf ER -