Research On Pedestrain Contour Tracking Method Based On RGBD Multimodal Information

Target tracking is one of the most challenging technologies in computer vision tasks,which belongs to intermediate processing tasks in computer vision tasks.Target tracking technology is an import part in many application fields such as intelligent monitoring,unmanned driving,human-computer interaction,virtual reality and so on.the so-called target tracking is that the target of interest is initially given in a series of video sequences,and target annotation in the sequence is continuously expressed in subsequent continuous video sequences,so as to achieve tracking effect.Contour tracking is a form of expressing the target with the contour of the edge.Compared with the boundingbox representation which is used more commonly,contour tracking can more flexibly reflect the shape change of the target,especially for non-rigid targets and it can give people more intuitive shape information.So contour tracking is of great research significance.But there are still many theoretical and technical problems to be solved,for example,tracking drift caused by the same appearance of target and background,weak ambient light and other factors etc.In this paper,the method of deep learning is used to do pedestrians contour tracking in video sequences,which fuse the appearance information with depth information,an end-to-end Two-stream networks structure is proposed,integrating information of RGB and Depth two modal to improve tracking effect.Main work is as follows:(1)Analyzing of existing video object segmentation algorithms based on deep learning and basic principle of active contour,research on two algorithms framework and design of pedestrians contour tracking method based on them,the advantages and disadvantages of the whole method are analyzed and the shortcomings of the whole method are pointed out.(2)Studying the existing video object segmentation deep learning algorithm OSVOS and improving it on the basis of its network structure so that the improved network can use Depth image information.At the same time,according to the characteristics of OSVOS algorithm and Depth image,we add a simple and practical update strategy to the original OSVOS principle,it is hoped that the improved network model can effectively use Depth information,thus,the whole pedestrians contour tracking method can achieve good tracking results in complex tracking scenarios such as insufficient light and similar target and background appearance.(3)The existing methods of multi-information fusion and convolution network of multi-modal information fusion are studied and analyzed.According to the common deep learning Two-stream convolution network structure,explore 3 different RGBD multimodal information fusion Two-stream fully convolution network structures.contour tracking methods based on 3 Two-stream network structure are compared on the test sets.The experimental results show that the tracking method based on adaptive fusion in decisionmaking stage has better tracking effect and better accuracy and robustness than the tracking method based on feature stage fusion.