Design Of Object Tracking Algorithm Based On Siamese Network And FPGA Verification

As a basic subject in the field of computer vision,object tracking task has great research value and application prospect.In recent years,deep learning technology has gradually matured,helping to make breakthroughs in various fields of computer vision.Siamese network is a object tracking algorithm framework that gives full play to the advantages of end-to-end of deep learning,with balanced performance and great research potential.Due to the online update mechanism are cancelled,it's performance is often limited by the extracted features,and the prediction accuracy of the algorithm can be enhanced by improving the feature extraction process.In addition,due to the deep convolutional neural network has a huge amount of computation and parameters,it's often applied to GPU.In order to better apply it,it's necessary to design the hardware accelerator based on the algorithm structure.A object tracking algorithm based on Siamese network is designed in the thesis,fully convolutional siamese network are used to extracting features,then the region proposal network are connected,the two branches are used to judge whether the border contains the object and the regression of the border shape.In order to obtain more robust and more powerful deep features,the following improvements are adopted: the features of multiple convolutional layers containing different information are fused,and the weight of the feature channel is adaptive distributed through the attention mechanism,make the model focus on the more important information when facing the object;In order to further enhance the accuracy,the result is modified by another region proposal network.In addition,the acceleration of convolutional neural network is designed based on the object tracking model.The model configurator,data scheduling module,high parallel convolution computing array and convolution result processing module are designed to accelerate the forward propagation process of convolutional neural networks.The chip resources are utilized as much as possible and the computing efficiency is improved.Finally,the verification system of object tracking algorithm based on FPGA is built,and the object tracking is completed under the cooperation control of hardware and software.The experimental results show that the overlap success rate of the object tracking algorithm designed in this thesis can reach 0.624 on the OTB2015 data set.The effective computing power of the convolutional neural network accelerator based on ZYNQ XC7Z035 FPGA chip can reach 128 GOPS,and the speed of the tracking system can reach 13 frames per second when the input picture size is 255×255.This thesis has reference value for the research of object tracking algorithm and system implementation based on convolutional neural network.