Wardrobe Model for Long Term Re-identification and Appearance Prediction

Long-term surveillance applications often involve having to re-identify individuals over several days or weeks. The task is made even more challenging with the lack of sufficient visibility of the subjects faces. We address this problem by modeling the wardrobe of individuals using discriminative features and labels extracted from their clothing information from video sequences. In contrast to previous person re-id works, we exploit that people typically own a limited amount of clothing and that knowing a person’s wardrobe can be used as a soft-biometric to distinguish identities. We a) present a new dataset consisting of more than 70,000 images recorded over 30 days of 25 identities; b) model clothing features using CNNs that minimize intra-garments variations while maximizing inter-garments differences; and c) build a reference wardrobe model that captures each persons set of clothes that can be used for re-id. We show that these models open new perspectives to long-term person re-id problem using clothing information.