first version

README.md

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 # GFS-DCF
 Matlab implementation of ICCV2019 paper "Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking"
+
+GFS-DCF Matlab implementation of the ICCV2019 paper
+"Joint Group Feature Selection and Discriminative Filter Learning for
+Robust Visual Object Tracking"
+
+Dependencies:
+MatConvNet, PDollar Toolbox. 
+Please download the latest MatConvNet (http://www.vlfeat.org/matconvnet/) 
+in './tracker_exter/matconvnet' 
+(Set 'opts.enableGpu = true' in 'matconvnet/matlab/vl_compilenn.m')
+
+Installation:
+Run install.m file to compile the libraries and download networks.
+
+Demo:
+Run demo.m 

demo.m

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+%% Demo : "Joint Group Feature Selection and Discriminative Filter Learning
+%% for Robust Visual Object Tracking"
+
+%% Please run install.m first
+close all;clear;clc;
+
+% Add involved folders into MATLAB path.
+setup_paths();
+
+% Choose and load video information
+base_path  = 'tracker_seque'; % (or OTB / VOT root path)
+
+video = choose_video(base_path);
+video_path = [base_path '/' video];
+[seq, gt_boxes] = load_video(video_path,video);
+
+% Run tracker
+results = run_GFSDCF(seq);
+
+% Evaluate performance
+pd_boxes = results.res;
+thresholdSetOverlap = 0: 0.05 : 1;
+success_num_overlap = zeros(1, numel(thresholdSetOverlap));
+if numel(gt_boxes(1,:))>4
+    temp = zeros(size(gt_boxes,1),4);  
+    for i = 1:size(gt_boxes,1)
+        bb8 = round(gt_boxes(i,:));
+        x1 = round(min(bb8(1:2:end)));
+        x2 = round(max(bb8(1:2:end)));
+        y1 = round(min(bb8(2:2:end)));
+        y2 = round(max(bb8(2:2:end)));
+        temp(i,:) = round([x1, y1, x2 - x1, y2 - y1]);
+    end
+    gt_boxes = temp;
+end
+
+thresholdSetPre = 0: 1 : 50;
+success_num_pre = zeros(1, numel(thresholdSetPre));
+res = calcRectInt(gt_boxes, pd_boxes);
+p_gt = [gt_boxes(:,2),gt_boxes(:,1)]+([gt_boxes(:,4),gt_boxes(:,3)]-1)/2;
+p_res = [pd_boxes(:,2),pd_boxes(:,1)]+([pd_boxes(:,4),pd_boxes(:,3)]-1)/2;
+dis = sqrt(sum((p_gt-p_res).^2,2));
+for t = 1: length(thresholdSetOverlap)
+    success_num_overlap(1, t) = sum(res > thresholdSetOverlap(t));
+end
+for t = 1: length(thresholdSetPre)
+    success_num_pre(1, t) = sum(dis <= thresholdSetPre(t));
+end
+Pre = success_num_pre(21) / size(gt_boxes, 1);
+cur_AUC = mean(success_num_overlap) / size(gt_boxes, 1);
+FPS_vid = results.fps;
+display([video  '----> '  'Rank/Frames: ' num2str(results.rank_var) '/' num2str(size(gt_boxes, 1)) ', FPS: ' num2str(FPS_vid)   ', AUC: '   num2str(cur_AUC)  ', DP: '   num2str(Pre)]);
+%gpuDevice([]);

install.m

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+% Compile libraries, download network modles and demo sequences for GFS-DCF
+[path_root, name, ext] = fileparts(mfilename('fullpath'));
+
+% mtimesx
+if exist('tracker_exter/mtimesx', 'dir') == 7
+    cd tracker_exter/mtimesx
+    mtimesx_build;
+    cd(path_root)
+end
+
+% PDollar toolbox
+if exist('tracker_exter/pdollar_toolbox/external', 'dir') == 7
+    cd tracker_exter/pdollar_toolbox/external
+    toolboxCompile;
+    cd(path_root)
+end
+
+% matconvnet
+if exist('tracker_exter/matconvnet/matlab', 'dir') == 7
+    cd tracker_exter/matconvnet/matlab
+    vl_compilenn; % enable/disable GPU based on your hardware
+    cd(path_root)
+
+    % donwload network
+    cd tracker_featu
+    mkdir offline_models
+    cd offline_models
+    if ~(exist('imagenet-resnet-50-dag.mat', 'file') == 2)
+        disp('Downloading the network "imagenet-resnet-50-dag.mat" from "http://www.vlfeat.org/matconvnet/models/imagenet-resnet-50-dag.mat"...')
+        urlwrite('http://www.vlfeat.org/matconvnet/models/imagenet-resnet-50-dag.mat', 'imagenet-resnet-50-dag.mat');
+        disp('Done!')
+    end
+    cd(path_root)
+else
+    error('GFS-DCF : Matconvnet not found.')
+end
+
+% download demo sequences
+if exist('tracker_seque', 'dir') == 0
+    mkdir tracker_seque
+    cd tracker_seque
+else 
+    cd tracker_seque
+end
+if exist('Biker', 'dir') == 0
+    disp('Downloading the demo sequence (1/2) "Biker" from "http://cvlab.hanyang.ac.kr/tracker_benchmark/seq/Biker.zip"...')
+    urlwrite('http://cvlab.hanyang.ac.kr/tracker_benchmark/seq/Biker.zip', 'Biker.zip');
+    unzip('Biker.zip');
+    rmdir('__MACOSX','s');
+    delete('Biker.zip');
+    disp('Done!')
+end
+if exist('bag', 'dir') == 0   
+    mkdir bag
+    cd bag
+    mkdir color
+    cd color
+    disp('Downloading the demo sequence (2/2) "Bag" from "http://data.votchallenge.net/sequences/28b56d282ad4abeaaca820b1bebcab2f2aeb7a9a8b0da71f1103f7853a0add7e80ea7d6030892616d0ca0a639366418d3587c5e55bf759be91c4cfb514d53751.zip"...')
+    urlwrite('http://data.votchallenge.net/sequences/28b56d282ad4abeaaca820b1bebcab2f2aeb7a9a8b0da71f1103f7853a0add7e80ea7d6030892616d0ca0a639366418d3587c5e55bf759be91c4cfb514d53751.zip', 'bag.zip');
+    unzip('bag.zip');
+    delete('bag.zip'); 
+    cd ..
+    urlwrite('http://data.votchallenge.net/vot2018/main/bag.zip', 'anno.zip');
+    unzip('anno.zip');
+    delete('anno.zip'); 
+    cd ..
+    disp('Done!')
+end
+cd(path_root)
+

run_GFSDCF.m

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+function results = run_GFSDCF(seq, res_path, bSaveImage, parameters)
+% Set tracker parameters and run tracker
+% Input  : seq[structure](sequence information)
+% Output : results[structure](tracking results)
+
+% Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler. Joint Group 
+% Feature Selection and Discriminative Filter Learning for Robust Visual Object 
+% Tracking. In ICCV 2019.
+
+% [results] = OTBsettings(seq);
+%% Set feature parameters
+% HOG feature settings
+hog_params.cell_size = 6;           
+hog_params.feature_is_deep = false;
+
+% CN feature settings
+cn_params.tablename = 'CNnorm';
+cn_params.useForGray = false;
+cn_params.cell_size = 4;
+cn_params.feature_is_deep = false;       
+
+% IC feature settings
+ic_params.tablename = 'intensityChannelNorm6';
+ic_params.useForColor = false;
+ic_params.cell_size = 4;
+ic_params.feature_is_deep = false;
+
+% CNN feature settings
+dagnn_params.nn_name = 'imagenet-resnet-50-dag.mat'; 
+dagnn_params.output_var = {'res4ex'};    
+dagnn_params.feature_is_deep = true;
+dagnn_params.augment.blur = 1;
+dagnn_params.augment.rotation = 1;
+dagnn_params.augment.flip = 1;
+
+params.t_features = {
+    struct('getFeature',@get_fhog,'fparams',hog_params),...
+    struct('getFeature',@get_table_feature, 'fparams',cn_params),...
+    struct('getFeature',@get_dagnn_layers, 'fparams',dagnn_params),...
+    struct('getFeature',@get_table_feature, 'fparams',ic_params),...
+};
+
+% Set [non-deep deep] parameters
+params.learning_rate = [0.6 0.05];          % updating rate in each learning stage
+params.channel_selection_rate = [0.9 0.075];% channel selection ratio
+params.spatial_selection_rate = [0.1 0.9];  % spatial units selection ratio
+params.output_sigma_factor = [1/16 1/4];	% desired label setting
+params.lambda1 = 10;                         % lambda_1
+params.lambda2 = 1;                         % lambda_2
+params.lambda3 = [16 12];                   % lambda_3
+params.stability_factor = [0 0];            % robustness testing parameter
+
+% Image sample parameters
+params.search_area_scale = [3.8 4.2];        % search region  
+params.min_image_sample_size = [150^2 200^2];% minimal search region size   
+params.max_image_sample_size = [200^2 250^2];% maximal search region size  
+
+% Detection parameters
+params.refinement_iterations = 1;           % detection numbers
+params.newton_iterations = 5;               % subgrid localisation numbers   
+
+% Set scale parameters
+params.number_of_scales = 7;                % scale pyramid size
+params.scale_step = 1.01;                   % scale step ratio
+
+% Set GPU 
+params.use_gpu = true;                 
+params.gpu_id = [];              
+
+% Initialisation
+params.vis_res = 1;                         % visualisation results
+params.vis_details = 1;                     % visualisation details for debug
+params.seq = seq;   
+
+% Run tracker
+[results] = tracker_main(params);

setup_paths.m

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+function setup_paths()
+% Add involved folders into MATLAB path.
+
+[pathstr, ~, ~] = fileparts(mfilename('fullpath'));
+
+% Tracker implementation
+addpath(genpath([pathstr '/tracker_imple/']));
+
+% Utilities
+addpath([pathstr '/tracker_utils/']);
+
+% The feature extraction
+addpath(genpath([pathstr '/tracker_featu/']));
+
+% Matconvnet
+addpath([pathstr '/tracker_exter/matconvnet/matlab/mex/']);
+addpath([pathstr '/tracker_exter/matconvnet/matlab']);
+addpath([pathstr '/tracker_exter/matconvnet/matlab/simplenn']);
+vl_setupnn;
+
+% PDollar toolbox
+addpath(genpath([pathstr '/tracker_exter/pdollar_toolbox/channels']));
+
+% Mtimesx
+addpath([pathstr '/tracker_exter/mtimesx/']);
+
+% mexResize
+addpath([pathstr '/tracker_exter/mexResize/']);
+
+% Networks and tables
+addpath([pathstr '/tracker_featu/offline_models/']);

tracker_exter/matconvnet/.gitattributes

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+* text=auto
+*.vcxproj text merge=union eol=crlf
+*.vcxproj.filters merge=union eol=crlf
+*.sln text merge=union eol=crlf

tracker_exter/matconvnet/.gitignore

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+*.xcodeproj/*xcuserdata*
+*.xcodeproj/project.xcworkspace/*xcuserdata*
+*.xcodeproj/project.xcworkspace/xcshareddata/
+mex/*
+mex
+data
+*.o
+*.pyc
+*~
+index.html
+matconvnet-*.tar.gz
+local
+contrib
+
+# Documentation
+doc/figures/svg/*.pdf
+doc/figures/*.idraw
+doc/.texpadtmp/*
+doc/*.pdf
+doc/.build
+
+# Website
+doc/site/docs/mfiles
+doc/site/site
+doc/site/.build
+doc/site/theme/css/bootstrap.min.css
+doc/site/theme/css/bootstrap.min.css.map
+doc/site/theme/css/font-awesome.min.css
+doc/site/theme/fonts/fontawesome-webfont.eot
+doc/site/theme/fonts/fontawesome-webfont.svg
+doc/site/theme/fonts/fontawesome-webfont.ttf
+doc/site/theme/fonts/fontawesome-webfont.woff
+doc/site/theme/fonts/fontawesome-webfont.woff2
+doc/site/theme/js/bootstrap.min.js
+doc/site/theme/js/jquery.min.js
+doc/site/theme/js/jquery.min.map
+doc/site/theme/js/npm.js
+
+# Visual C
+*.suo
+*.user
+*.sdf
+*.opensdf
+doc/figures/svg/*.idraw
+