基于近邻中心迭代策略的单标注样本视频行人重识别
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王洪元,E-mail:hywang@cczu.edu.cn

基金项目:

国家自然科学基金项目(61976028,61572085,61806026,61502058);江苏省自然科学基金项目(BK20180956);社会安全信息感知与系统工业和信息化部重点实验室(南京理工大学)创新基金(202004).


One-Shot Video-Based Person Re-Identification Based on Neighborhood Center Iteration Strategy
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the National Natural Science Foundation of China (61976028, 61572085, 61806026, 61502058); the Natural Science Foundation of Jiangsu Province (BK20180956); Key Laboratory Foundation of Information Perception and Systems for Public Security of MIIT (Nanjing University of Science and Technology) (202004)

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    摘要:

    为解决视频行人重识别数据集标注困难的问题,本文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,提出了一个新的标签评估方法: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;同时提出了一个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本文方法相比于最新的先进方法在性能上得到一个非常好的提升.

    Abstract:

    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 paper, 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 paper, 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, which demonstrate that our methods outperform the state-of-the-art methods.

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张云鹏,王洪元,张继,陈莉,吴琳钰,顾嘉晖,陈强.基于近邻中心迭代策略的单标注样本视频行人重识别.软件学报,,():0

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  • 收稿日期:2020-01-15
  • 最后修改日期:2020-04-19
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  • 在线发布日期: 2020-10-12
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