Person Reidentification Using Attribute-Restricted Projection Metric Learning.

Person reidentification matches person observations captured by nonoverlapping cameras at different times and locations. Due to the appearance variations caused by view angle, pose, lighting, background, and occlusion, person reidentification is a challenging task. To improve the accuracy of person reidentification, this paper proposes a metric learning method. The main idea of our method is to learn a low-dimensional metric space, in which the features extracted from different observations of the same person are pulled, and the features extracted from observations of different persons but in the neighborhood of original feature space are pushed. Then all the features are projected to this metric space for matching. Compared with other metric learning methods, our method differentiates negative samples according to their importances in constructing the metric space, and pays more attention to restrain the more confusable negative pairs. Moreover, our method can simultaneously learn a uniform metric space for multiple features that is practical for multifeature fusion, an effective way to improve reidentification performance. Experimental results on three popular data sets show that our method is more robust against appearance changes and it outperforms other state-of-the-art reidentification methods. [ABSTRACT FROM AUTHOR]