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  -