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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. 

We present a novel approach to multi-person multi-camera tracking based on learning the space-time continuum of a camera network. Some challenges involved in tracking multiple people in real scenarios include a) ensuring reliable continuous association of all persons, and b) accounting for presence of blind-spots or entry/exit points. Most of the existing methods design sophisticated models that require heavy tuning of parameters and it is a nontrivial task for deep learning approaches as they cannot be applied directly to address the above challenges. Here, we deal with the above points in a coherent way by proposing a discriminative spatio-temporal learning approach for tracking based on person re-identification using LSTM networks. This approach is more robust when no a-priori information about the aspect of an individual or the number of individuals is known. The idea is to identify detections as belonging to the same individual by continuous association and recovering from past errors in associating different individuals to a particular trajectory. We exploit LSTM's ability to infuse temporal information to predict the likelihood that new detections belong to the same tracked entity by jointly incorporating visual appearance features and location information. The proposed approach gives a 50% improvement in the error rate compared to the previous state-of-the-art method on the CamNeT dataset and 18% improvement as compared to the baseline approach on DukeMTMC dataset.