Abstract: In the process of moving target detection, the background is complex and the target is easily occluded. This paper proposes an autonomous detection algorithm for moving targets based on unsupervised missing value prediction. The missed targets are regarded as missing values in the tag data. According to the prior knowledge of the category and number of objects to be detected, the unsupervised generative adversarial imputation networks (GAIN) are used to predict the missing values through the acquired tag data, which greatly improves the recall rate at the expense of less accuracy. The experimental results on the small sample dataset of the characteristic parts of cattle show when the missing rate of tag data is less than 40%, the accuracy of missing value prediction is about 95%, and the average F1 score of detection is 0.92 for different degrees of occluded targets. This method has good detection performance for moving targets under the condition of small samples, which can reduce the uncertainty in practical application and the dependence of the algorithm on sample data, and improve the problem of missing detection in the process of moving target detection.

Key words: small sample, unsupervised learning, generative adversarial imputation networks, missing value prediction, moving target detection

摘要： 针对运动目标检测过程中由于背景复杂、目标易发生遮挡而产生的漏检问题，提出一种基于无监督缺失值预测的运动目标检测算法。将漏检的目标视为标签数据中的缺失值，根据待检测目标的类别和数量，利用无监督的生成对抗插补网络（generative adversarial imputation networks, GAIN），通过已获取的标签数据对缺失值进行预测，以牺牲较少的精确率为代价大幅提高召回率。在小样本的牛只特征部位数据集上的实验结果表明，在标签数据缺失率低于40%的情况下，缺失值预测的准确率约为95%，对于不同程度的被遮挡目标，检测的平均F1分数为0.92。该方法在小样本条件下，对运动目标具有较好的检测性能，可减小实际应用中的不确定性，以及算法对样本数据的依赖性，改善运动目标检测过程中的漏检问题。

关键词: 小样本, 无监督学习, 生成对抗插补网络, 缺失值预测, 运动目标检测