Person Re-Identification Using Kernel-Based Metric Learning Methods
COMPUTER VISION - ECCV 2014, PT VII(2014)
摘要
Re-identification of individuals across camera networks with limited or no overlapping fields of view remains challenging in spite of significant research efforts. In this paper, we propose the use, and extensively evaluate the performance, of four alternatives for re-ID classification: regularized Pairwise Constrained Component Analysis, kernel Local Fisher Discriminant Analysis, Marginal Fisher Analysis and a ranking ensemble voting scheme, used in conjunction with different sizes of sets of histogram-based features and linear,chi(2) and RBF-chi(2) kernels. Comparisons against the state-of-art show significant improvements in performance measured both in terms of Cumulative Match Characteristic curves (CMC) and Proportion of Uncertainty Removed (PUR) scores on the challenging VIPeR, iLIDS, CAVIAR and 3DPeS datasets.
更多查看译文
关键词
Feature Vector,Ranking Algorithm,Camera Network,Hinge Loss,Scatter Matrice
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络