Scalable people re-identification based on a one-against-some classification scheme
People re-identification is a problem of increasing interest in computer vision, mainly in applications such as video surveillance and dynamic environment monitoring. However, the large amount of data captured from multiple cameras, the large number of agents involved and poor acquisition conditions make it a difficult problem to solve. Recent works have shown that the use of multiple feature extraction methods combined by a weighting technique considering a one-against-all classification scheme provide accurate results for applications such as face recognition and appearance-based modeling. However, to enroll new subjects, all models need to be rebuild, which results in an increasingly computational time. To reduce this problem, this work proposes a classification scheme, called one-against-some, to allow scalable enrollment of new individuals without reducing the accuracy when compared to the one-against-all classification scheme.