Re-Identification for Online Person Tracking by Modeling Space-Time Continuum.

We present a novel approach to multi-person multicamera 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.