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

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