1.算法概述

1.正确版本组合：Win7+Matlab R2015b+CUDA7.5+vs2013

CUDA7.5下载地址为：

http://developer.download.nvidia.com/compute/cuda/7.5/Prod/local_installers/cuda_7.5.18_windows.exe

vs2013要专业版。

如下所示：

全部安装好之后，做如下操作：

2.CPU配置，运行CNN工具箱中的，然后再运行可以完成CPP文件的编译。

3.编译成功后，会产生

这些必须在电脑上编译，否则别人的复制给你，如果配置不一样，可能会报错。

4.GPU配置：

安装cudnn：https://developer.nvidia.com/rdp/cudnn-archive，放到CNN工具箱中的新建local文件夹中，

然后mex -setup下，操作和CPU一样。

然后执行matlab程序：

vl_setupnn;

vl_compilenn('enableGpu', true,'cudaRoot', 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5','cudaMethod', 'nvcc', 'enableCudnn', 'true','cudnnRoot', 'E:\A_2016_FPGA_Test\FC_tracking\A_FC\matconvnet-1.0-beta20\local\cudnn');

红色部分为你的路径，不要有中文。

5.然后运行我给的程序，就可以自动运行了，原来程序的运行非常麻烦，我定义了这个程序，可以一步运行处结果。

2.仿真效果预览

Win7+Matlab R2015b+CUDA7.5+vs2013

运行后，如果可以出现如下结果：

3.核心MATLAB代码预览

% -------------------------------------------------------------------------------------------------
function bboxes = tracker(varargin)
%TRACKER
%   is the main function that performs the tracking loop
%   Default parameters are overwritten by VARARGIN
%
%   Luca Bertinetto, Jack Valmadre, Joao F. Henriques, 2016
% -------------------------------------------------------------------------------------------------% These are the default hyper-params for SiamFC-3S% The ones for SiamFC (5 scales) are in params-5s.txtp.numScale = 3;p.scaleStep = 1.0375;p.scalePenalty = 0.9745;p.scaleLR = 0.59; % damping factor for scale updatep.responseUp = 16; % upsampling the small 17x17 response helps with the accuracyp.windowing = 'cosine'; % to penalize large displacementsp.wInfluence = 0.176; % windowing influence (in convex sum)p.net = '2016-08-17.net.mat';%% execution, visualization, benchmarkp.video = 'vot15_bag';p.visualization = false;p.gpus = 1;p.bbox_output = false;p.fout = -1;%% Params from the network architecture, have to be consistent with the trainingp.exemplarSize = 127;  % input z sizep.instanceSize = 255;  % input x size (search region)p.scoreSize = 17;p.totalStride = 8;p.contextAmount = 0.5; % context amount for the exemplarp.subMean = false;%% SiamFC prefix and idsp.prefix_z = 'a_'; % used to identify the layers of the exemplarp.prefix_x = 'b_'; % used to identify the layers of the instancep.prefix_join = 'xcorr';p.prefix_adj = 'adjust';p.id_feat_z = 'a_feat';p.id_score = 'score';% Overwrite default parameters with vararginp = vl_argparse(p, varargin);
% -------------------------------------------------------------------------------------------------% Get environment-specific default paths.p = env_paths_tracking(p);1% Load ImageNet Video statisticsif exist(p.stats_path,'file')stats = load(p.stats_path);elsewarning('No stats found at %s', p.stats_path);stats = [];end2% Load two copies of the pre-trained networknet_z = load_pretrained([p.net_base_path p.net], p.gpus);3net_x = load_pretrained([p.net_base_path p.net], []);4p.seq_base_pathp.video[imgFiles, targetPosition, targetSize] = load_video_info(p.seq_base_path, p.video);nImgs = numel(imgFiles);startFrame = 1;5% Divide the net in 2% exemplar branch (used only once per video) computes features for the targetremove_layers_from_prefix(net_z, p.prefix_x);remove_layers_from_prefix(net_z, p.prefix_join);remove_layers_from_prefix(net_z, p.prefix_adj);% instance branch computes features for search region x and cross-correlates with z features6remove_layers_from_prefix(net_x, p.prefix_z);zFeatId = net_z.getVarIndex(p.id_feat_z);scoreId = net_x.getVarIndex(p.id_score);% get the first frame of the videoim = gpuArray(single(imgFiles{startFrame}));% if grayscale repeat one channel to match filters sizeif(size(im, 3)==1)im = repmat(im, [1 1 3]);end% Init visualizationvideoPlayer = [];if p.visualization && isToolboxAvailable('Computer Vision System Toolbox')videoPlayer = vision.VideoPlayer('Position', [100 100 [size(im,2), size(im,1)]+30]);end7% get avg for paddingavgChans = gather([mean(mean(im(:,:,1))) mean(mean(im(:,:,2))) mean(mean(im(:,:,3)))]);wc_z = targetSize(2) + p.contextAmount*sum(targetSize);hc_z = targetSize(1) + p.contextAmount*sum(targetSize);s_z = sqrt(wc_z*hc_z);scale_z = p.exemplarSize / s_z;% initialize the exemplar[z_crop, ~] = get_subwindow_tracking(im, targetPosition, [p.exemplarSize p.exemplarSize], [round(s_z) round(s_z)], avgChans);if p.subMeanz_crop = bsxfun(@minus, z_crop, reshape(stats.z.rgbMean, [1 1 3]));endd_search = (p.instanceSize - p.exemplarSize)/2;pad = d_search/scale_z;s_x = s_z + 2*pad;% arbitrary scale saturationmin_s_x = 0.2*s_x;max_s_x = 5*s_x;switch p.windowingcase 'cosine'window = single(hann(p.scoreSize*p.responseUp) * hann(p.scoreSize*p.responseUp)');case 'uniform'window = single(ones(p.scoreSize*p.responseUp, p.scoreSize*p.responseUp));end% make the window sum 1window = window / sum(window(:));scales = (p.scaleStep .^ ((ceil(p.numScale/2)-p.numScale) : floor(p.numScale/2)));% evaluate the offline-trained network for exemplar z featuresnet_z.eval({'exemplar', z_crop});z_features = net_z.vars(zFeatId).value;z_features = repmat(z_features, [1 1 1 p.numScale]);bboxes = zeros(nImgs, 4);% start trackingtic;for i = startFrame:nImgsiif i>startFrame% load new frame on GPUim = gpuArray(single(imgFiles{i}));% if grayscale repeat one channel to match filters sizeif(size(im, 3)==1)im = repmat(im, [1 1 3]);endscaledInstance = s_x .* scales;scaledTarget = [targetSize(1) .* scales; targetSize(2) .* scales];% extract scaled crops for search region x at previous target positionx_crops = make_scale_pyramid(im, targetPosition, scaledInstance, p.instanceSize, avgChans, stats, p);% evaluate the offline-trained network for exemplar x features[newTargetPosition, newScale] = tracker_eval(net_x, round(s_x), scoreId, z_features, x_crops, targetPosition, window, p);targetPosition = gather(newTargetPosition);% scale damping and saturations_x = max(min_s_x, min(max_s_x, (1-p.scaleLR)*s_x + p.scaleLR*scaledInstance(newScale)));targetSize = (1-p.scaleLR)*targetSize + p.scaleLR*[scaledTarget(1,newScale) scaledTarget(2,newScale)];else% at the first frame output position and size passed as input (ground truth)endrectPosition = [targetPosition([2,1]) - targetSize([2,1])/2, targetSize([2,1])];% output bbox in the original frame coordinatesoTargetPosition = targetPosition; % .* frameSize ./ newFrameSize;oTargetSize = targetSize; % .* frameSize ./ newFrameSize;bboxes(i, :) = [oTargetPosition([2,1]) - oTargetSize([2,1])/2, oTargetSize([2,1])];%         if p.visualizationif isempty(videoPlayer)figure(1), imshow(im/255);figure(1), rectangle('Position', rectPosition, 'LineWidth', 4, 'EdgeColor', 'y');drawnowfprintf('Frame %d\n', startFrame+i);elseim = gather(im)/255;im = insertShape(im, 'Rectangle', rectPosition, 'LineWidth', 4, 'Color', 'yellow');% Display the annotated video frame using the video player object.step(videoPlayer, im);end
%         endif p.bbox_outputfprintf(p.fout,'%.2f,%.2f,%.2f,%.2f\n', bboxes(i, :));endendbboxes = bboxes(startFrame : i, :);end
A000

4.完整MATLAB程序

matlab源码说明_我爱C编程的博客-CSDN博客

基于全卷积Fully-Convolutional-Siamese-Networks的目标跟踪仿真相关推荐

自动驾驶系统进阶与项目实战（三）基于全卷积神经网络的点云三维目标检测和ROS实战
自动驾驶系统进阶与项目实战(三)基于全卷积神经网络的点云三维目标检测和ROS实战前面入门系列的文章中我介绍了几种点云三维分割/目标检测模型,在做点云预处理上,有通过球面投射(SqueezeNet)得 ...
吴恩达深度学习系列--卷积神经网络(Convolutional Neural Networks)-03(目标检测)
目标检测 3.1 目标定位(Object localization) 3.1.1概念 3.1.2 监督学习任务定义目标标签y 3.2 特征点检测(Landmark detection) 3.3 基于滑 ...
[论文翻译]V-Net:Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
论文下载: 地址 V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation V-Net: ...
基于全卷积神经网络的前列腺磁共振图像分割
(本科毕业论文题目.摘要.关键词及相应的英文翻译,欢迎做前列腺分割的小伙伴可以加我qq:604395564一起交流呀) 基于全卷积神经网络的前列腺磁共振图像分割目的研究从前列腺磁共振图像中自动分割的 ...
Fully Convolutional Adaptation Networks for Semantic Segmentation
参考论文解析之<Fully Convolutional Adaptation Networks for Semantic Segmentation> - 云+社区 - 腾讯云论文网址 ...
Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks阅读笔记
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Ne ...
人群分割--Fully Convolutional Neural Networks for Crowd Segmentation
Fully Convolutional Neural Networks for Crowd Segmentation https://arxiv.org/abs/1411.4464 这里设计了一个全卷 ...
深度学习之卷积神经网络(Convolutional Neural Networks, CNN)
前面, 介绍了DNN及其参数求解的方法(深度学习之 BP 算法),我们知道DNN仍然存在很多的问题,其中最主要的就是BP求解可能造成的梯度消失和梯度爆炸.那么,人们又是怎么解决这个问题的呢?本节的卷积 ...
卷积神经网络Convolutional Neural Networks深度解析I
知识的广度来自知识的深度,学习如果不成体系那是多可怕的一件事儿,希望我们在未来的学习道路上坚守初心,不要给自己留下遗憾,以自己喜欢的方式生活,做自己喜欢做的事,宠爱自己,做一个独一无二的自己! 对于文 ...
《DeepLearning.ai》第十课：卷积神经网络(Convolutional Neural Networks)
第十课:卷积神经网络(Convolutional Neural Networks) 1.1 计算机视觉(Computer vision) 通常如果处理大图用传统的神经网络需要特别大的输入,因此需要大量 ...

基于全卷积Fully-Convolutional-Siamese-Networks的目标跟踪仿真

1.算法概述

2.仿真效果预览

3.核心MATLAB代码预览

4.完整MATLAB程序

基于全卷积Fully-Convolutional-Siamese-Networks的目标跟踪仿真相关推荐

最新文章

热门文章