点击次数：

论文类型：期刊论文

发表时间：2019-10-01

发表刊物：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录刊物：SCIE

卷号：30

期号：10

页面范围：2999-3009

ISSN号：2162-237X

关键字：Metric learning; person reidentification; view transformation

摘要：Person reidentification is of great importance in visual surveillance and multiperson tracking across multiple camera views. Two fundamental problems are critical for person reidentification: 1) how to account for appearance variation or feature transformation caused by viewpoint changes and 2) how to learn a discriminative distance metric for reidentification. In this paper, we propose an algorithm in which both feature transformation and metric learning are exploited and jointly optimized. We learn local models from subsets of training samples with regularization imposed by the global model which is trained among the entire data set. The learned local models enhance the discriminative strength and generalization ability. Experimental results on the Viewpoint Invariant PEdestrian Eecognition, Queen Mary University of London ground reidentification, CUHK01, and CUHK03 benchmark data sets show that the proposed sample-specific view-invariant approach performs favorably against the state-of-the-art person reidentification methods.