Learning to recognize people in a smart environment

In this paper, we address the problem of online learning to recognize people from visual appearances, a prerequisite step towards building a fully intelligent and context-aware smart environment. While the trajectories of tracked individuals are responsible for producing samples to the appearance signature learning process, it is highly risky to directly label these appearance samples with tracker IDs, due to possible tracker switches and temporary tracker losses. Through the exploration of trajectory fidelity in terms of temporal continuity and spatial locality, we show that the side information from tracking, in the form of pairwise constraints, such as “must-link” and “cannot-link”, could significantly benefit signature learning. Furthermore, to learn and update an online identity signature pool, a two-step approach is proposed: 1) a data clustering step based on spectral kernel learning with pairwise constraints, and 2) a large-margin based discriminative signature model learning step. A real-world setup in a smart office environment is used to evaluate the performance of the learning paradigm. Consistent recognition of individuals from live videos verifies the efficacy and effectiveness of our proposal.