Research And System Implementation Of Tracking System Based On Multi-feature-Fusion Network And Object Proposal

Single target tracking is a basic hot research area in the field of computer vision.Its task is to find a target from a video sequence in which the target is annotated in the first frame.With the development of robotics,autopilot,video processing and weapons guidance,tracking technology becomes more and more concerned by academia and industry.After analyzing the advantages and disadvantages of the traditional tracking algorithm,this paper proposes a tracking method based on multi semantic feature fusion and object proposal.This method is based on Siamese neural network.To overcome Siamese network’s shortcomings,we adjust the network structure,and change the way training sets are organized,making the network more expressive and discriminative.We also propose a fast full-graph re-search strategy to deal with the loss of targets.Our work can be divided into four parts:the improvement of Siamese network,object appearance extraction based on color histogram,full graph searching strategy based on object proposal,design and implementation of tracking system.(1)The improvement of Siamese network.We analyze the tracking algorithm based on traditional Siamese network,and point out its working principle and shortcomings.According to the shortcomings of Siamese network,we improve its performance from several aspects.We add multi-layer semantic fusion structure to the network,which makes the network aware from the appearance and category of objects.We add attention structure based on similarity to make the network suppress background noise and encode more discriminative features.(2)Object appearance extraction based on color histogram.In this paper,an object appearance extraction strategy based on color histogram is proposed.The appearance of the object is described by recording the occurrence probability of the color of the object.According to the color of the pixel in the search area,the pixel-level probability map of the object can be generated to constrain the output of the neural network.(3)In this paper,a framework of occlusion edge extraction,object proposal and full graph searching is proposed to deal with the situation of target out of sight.By using the semantic information occlusion edge to judge the possibility of objects in the bounding box,the concept of objectness is introduced into the tracking task,and the target search results of the whole image are obtained with less computational cost.(4)Design and implementation of tracking system.The tracking algorithm proposed in this paper is tested on OTB2013,OTV2015,VOT2016,VOT2017,VOT2018 and other internationally renowned tracking data sets.Compared with similar algorithms published in the flagship conference of computer vision in recent years,our algorithm shows better performance.Finally,a tracking system is designed by using the above algorithm.