Research On Visual Object Tracking Algorithm Based On Siamese Network

Visual object tracking has been a very active research direction in the computer field,and it has been widely used in many fields such as video surveillance,unmanned driving,human-computer interaction,drone surveillance,augmented reality,and robotics.Because the actual application scene is complex and changeable,there are interference factors such as occlusion,deformation,background clutter,light changes,fast motion,and motion blur,which makes designing a fast,accurate,and robust target tracking algorithm still a very challenging task.The content of this paper is single object tracking,specifically,the manually marked object position is given in the first frame,and the object position is estimated accurately,in real time,and robustly in the next video frame.In order to fully utilize the advantages of convolutional neural network to extract features and realize end-to-end training tracker in the field of visual object tracking,the object template and the feature representation learned by the search area are cross-correlated based on the siamese network tracking algorithm to object Tracking translates into similarity learning problems.The main research work and innovations of this article are as follows:(1)Aiming at the problem of low discrimination and positioning accuracy of the existing siamese network,it is proposed to use deep convolutional neural network to extract features,adopt effective spatial perception sampling strategy and residual unit to eliminate spatial position deviation,according to the size of the object template The size of the backbone network structure is set to the appropriate receptive field and the total step size of the network;(2)A siamese network object tracking algorithm based on Res Net and multi-layer feature weighted fusion is proposed.Use GAN network to solve the imbalance of positive and negative sample categories of training data;use the deep separable cross-correlation algorithm to deal with the imbalance of the number of parameters of the two branches of the network;use multi-layer feature weighted fusion,local-global search strategy to improve the tracker's Discrimination and resolution;(3)A siamese network object tracking algorithm based on Google Net and Head structure is proposed.Spatial sensing sampling strategy is adopted to overcome the error caused by deep convolutional network filling;in the case of real-time tracking,the headstructure,object state quality estimation function and data augmentation algorithm are used to improve the robustness and positioning accuracy of the tracker.