近邻中心迭代策略的单标注视频行人重识别

为解决视频行人重识别数据集标注困难的问题，提出了基于单标注样本视频行人重识别的近邻中心迭代策略.该策略逐步利用伪标签视频片段迭代更新网络结构，以获得最佳的模型.针对预测无标签视频片段的伪标签准确率低的问题，提出了一种标签评估方法：每次训练后，将所选取的伪标签视频片段和有标签视频片段特征中每个类的中心点作为下一次训练中预测伪标签的度量中心点；同时提出基于交叉熵损失和在线实例匹配损失的损失控制策略，使得训练过程更加稳定，无标签数据的伪标签预测准确率更高.在MARS，DukeMTMC-VideoReID这两个大型数据集上的实验验证了该方法相比于最新的先进方法，在性能上得到非常好的提升.

One-shot Video-based Person Re-identification Based on Neighborhood Center Iteration Strategy

In order to solve the problem of labeling difficulty in video-based person re-identification dataset, a neighborhood center iteration strategy based on one-shot video-based person re-identification is proposed in this study, which gradually optimizes the network by using pseudo-labeled tracklets to obtain the best model. Aiming at the problem that the accuracy of predicting pseudo labels of unlabeled tracklets is low, a novel label evaluation method is proposed. After each training, the center points of each class in the features of the selected pseudo-labeled tracklets and labeled tracklets are used as the measurement center points for predicting the pseudo labels in the next training. At the same time, a loss control strategy based on cross entropy loss and online instance matching loss is proposed in this study, which makes the training process more stable and the accuracy of the pseudo labels higher. Experiments are implemented on two large datasets: MARS and DukeMTMC-VideoReID, and the result demonstrates that the proposed method outperforms the current state-of-the-art methods.