Introducing Depth Information Into Generative Target Tracking - Sun, Dongyue; Wang, Xian; Lin, Yonghong; Yang, Tianlong; Wu, Shixu (Frontiers in Neurorobotics, 2021)
Common visual features used in target tracking, including color, grayscale, etc., are prone to fail in a confusingly similar-looking background. In recent years, as the technology of three-dimensional visual information acquisition gradually gains ground, the condition for depth information to be widely used in target tracking has been available. This research focuses on discussing the possible ways to introduce depth information into the generative target tracking methods based on kernel density estimation as well as the performance of different ways of introduction, so as to provide a reference for the use of depth information in actual target tracking systems. The article started with an analysis of the Mean-Shift technical framework, a typical algorithm of generative target tracking; then proposed four methods to introduce depth information: thresholding of data source, thresholding of the density distribution of data set, weighting of data source, and weighting of the density distribution of data set; and ended with an experimental study made to evaluate the validity, characteristics and advantages of every method in detail. The experimental results showed that the four methods could improve the validity of the basic method to a certain extent and meet the requirement of real-time target tracking in a confusingly similar background. The method of weighting the density distribution of data set, into which depth information is introduced, is the prime choice in engineering practice for it delivers excellent comprehensive performance and the highest accuracy, whereas methods, the thresholding of data source and the thresholding of the density distribution of data set, are less time-consuming. The research conclusions also provide a reference for the improvement of other target tracking methods into which depth information can be introduced.