Person re-identification using matrix completion

Person re-identification is a challenging problem in multicamera surveillance systems. In this paper, we formulate person re-identification as a cross-camera feature construction problem to overcome the feature variation between different camera spaces. The linear transformation of color information between probe and gallery camera spaces makes the stacked matrix, which concatenates features from these two camera spaces, rank deficient. From the feature observed in probe camera space we can construct its corresponding feature in gallery camera space by completing unknown entries on the relevant positions of the stacked matrix, and then match the constructed probe feature with features in gallery camera space. We also introduce additive noise term into the model to deal with the adverse effects caused by illumination variation with time. Experimental results demonstrate the proposed approach outperforms the metric learning methods as well as simple nearest neighbor search, and obtains a competitive performance compared with the state-of-the-art methods.