Weighted hybrid features for person re-identification

In video-surveillance, person re-identification is described as the task of recognizing distinct individuals over a network of cameras. It is an extremely challenging task since visual appearances of people can change significantly when viewed in different cameras. Many person re-identification methods offer distinct advantages over each other in terms of robustness to lighting, scale and pose variations. Keeping this consideration in mind, this paper proposes an effective new person reidentification model which incorporates several recent state-of-the-art feature extraction methodologies such as GOG, WHOS and LOMO features into a single framework. Effectiveness of each feature type is estimated and optimal weights for the similarity measurements are assigned through a multiple metric learning method. The proposed re-identification approach is then tested on multiple benchmark person re-identification datasets where it outperforms many other state-of-the-art methodologies.