An ensemble of approaches for reliable person re-identification is proposed in this paper. The proposed ensemble is built combining widely used person re-identification systems using different color spaces and some variants of state-of-the-art approaches that are proposed in this paper. Different descriptors are tested, and both texture and color features are extracted from the images; then the different descriptors are compared using different distance measures (e.g., the Euclidean distance, angle, and the Jeffrey distance). To improve performance, a method based on skeleton detection, extracted from the depth map, is also applied when the depth map is available. The proposed ensemble is validated on three widely used datasets (CAVIAR4REID, IAS, and VIPeR), keeping the same parameter set of each approach constant across all tests to avoid overfitting and to demonstrate that the proposed system can be considered a general-purpose person re-identification system. Our experimental results show that the proposed system offers significant improvements over baseline approaches.
Information Technology and Cybersecurity
© 2015 The authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
person re-identification, texture descriptors, ensemble, color space, depth map
Nanni, Loris, Matteo Munaro, Stefano Ghidoni, Emanuele Menegatti, and Sheryl Brahnam. "Ensemble of different approaches for a reliable person re-identification system." Applied Computing and Informatics 12, no. 2 (2016): 142-153.
Applied Computing and Informatics