计算机技术与发展2024，Vol.34Issue(4)：35-41,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0006
基于弱监督表示学习的热红外目标跟踪
Weakly Supervised Based Representation Learning for Thermal Infrared Target Tracking
摘要
Abstract
Since thermal infrared imaging technology has a stronger ability to penetrate fog,haze,rain and snow,the imaging effect is almost unaffected in bad weather conditions,which makes the target tracking task based on thermal infrared images has been paid more and more attention by researchers.Aiming at the problem of insufficient labeled data in the model training of the thermal infrared target tracking algorithm based on convolutional neural network,a method based on Weakly Supervised Representation Learning(WSRL)is proposed,which uses a small amount of labeled data and a mass of unlabeled data for model training,so as to be used in thermal infrared target tracking tasks.Firstly,the guidance of active learning is used to select the most representative training samples from a large amount of unlabeled data.Then,given the ground-truth label of the target in the first frame of each sample sequence,the basic tracker is used to generate pseudo-labels for other frames in the same sequence.Then,the training data with ground-truth labels and pseudo-labels is used for model training.Finally,the trained model is used to test the algorithm on the thermal infrared target tracking algorithm test data set.The experimental results show that the proposed method can ensure the accuracy of the tracker while reducing the demand for label data for model training.关键词
弱监督表示学习/主动学习/训练样本挑选/伪标签生成/热红外目标跟踪Key words
weakly supervised representation leaming/active learning/training sample selection/pseudo-label generation/thermal infrared target tracking分类
计算机与自动化引用本文复制引用
袁笛..基于弱监督表示学习的热红外目标跟踪[J].计算机技术与发展,2024,34(4):35-41,7.基金项目
国家自然科学基金(62202362) （62202362）
中国博士后科学基金(2021TQ0247) （2021TQ0247）
中央高校基本科研业务费专项资金(XJS222503) （XJS222503）
