Research On Object Tracking Algorithm Based On Siamese Networks And Its Application In Ship Scene

Object tracking is a hot topic in computer vision.In recent years,lots of excellent object tracking algorithms have emerged,research in the field of object tracking has yielded substantial results.However,tracking algorithms with high accuracy always show slow tracking speed,these algorithms cannot meet the requirements of real-time tracking in the application scenario.This thesis aims to achieve a balance between the accuracy and speed of the tracking algorithm,that is,to achieve competitive tracking accuracy with several state-of-the-art trackers under the premise of real-time tracking.In order to achieve this research objective,this thesis deeply studies the object tracking algorithm based on siamese networks,integrates correlation filter methods,uses cascaded classifier to achieve re-detection,and successfully combines the advantages of deep neural network to extract good features and fast speed of correlation filtering,greatly improves the tracking accuracy within real-time tracking.The main work and innovations of this thesis can be summarized as follows:(1)An arbitrary object tracking algorithm based on siamese networks with correlation filter and re-detection is proposed.We improve the fully-convolutional siamese networks for object tracking algorithm,optimize the structure of the upper and lower subnetworks in the siamese structure,incorporate correlation filter,add a re-detection module,and propose a tracking algorithm based on siamese networks with correlation filter and re-detection.Finally,large quantities of comparison experiments are done on OTB-2013(Online Tracking Benchmark 2013),and quantitative and qualitative analyses are also performed.The experimental results show that the proposed algorithm successfully balances the tracking accuracy and speed,reaches state-of-the-art tracking accuracy while achieving a real-time tracking speed.(2)Siamese networks are applied to the ship object tracking scenario.We apply the arbitrary object tracking algorithm based on siamese networks with correlation filter and re-detection to ship tracking scene.During the off-line training phase of model,a large number of manually-labeled ship video sequences are used.According to the characteristics of ship navigation,sea-sky-line detection technology is used to narrow the object search area,and the frequency of re-detection is also optimized.Finally,as there is no publicly available tracking benchmark for ship scene,Ship Benchmark is built in this thesis to compare and analyze the algorithm,and it verifies the effectiveness of siamese networks in ship tracking scene.