Weakly Supervised Based Representation Learning for Thermal Infrared Target Tracking
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.
万方数据
维普
