关键词: 行人再识别, 度量学习算法, 半监督学习, 交叉二次判别分析, 统计推断, 识别精度

Abstract:

For person re-identification task cross camera view, existing metric learning based methods are biased to estimation of model parameters due to the small sample size problem. In this paper, it proposes a normality resampling based cross-view quadratic discriminant analysis method which is a semi-supervised metric learning based method. Moreover, the proposed method is tested on the publicly released datasets VIPeR and CUHK01. The test results show that the proposed method is better than existing cross-view quadratic discriminant analysis method. Especially on the rank-1 identification rate, the proposed method overcomes the state-of-the-art comparative methods MLAPG and NFST method 7.79% and 4.68% respectively. Besides, the proposed method is more robust to the training data size.

Key words: person re-identification, metric learning method, semi-supervised learning, cross-view quadratic discriminant analysis, statistical inference, recognition accuracy