run_ADTrack_v2.m

function results = run_ADTrack_v2(seq)

%  Initialize path
addpath('feature/');
addpath('implementation/');
addpath('utils/');
%  HOG feature parameters
hog_params.cell_size = 4;
hog_params.nDim   = 31;
%  ColorName feature parameters
cn_params.nDim  =10;
cn_params.tablename = 'CNnorm';
cn_params.useForGray = false;
cn_params.cell_size = 4;
%  Grayscale feature parameters
grayscale_params.nDim=1;
grayscale_params.colorspace='gray';
grayscale_params.cell_size = 4;

% Global feature parameters 
params.t_features = {
    struct('getFeature',@get_colorspace, 'fparams',grayscale_params),...  
    struct('getFeature',@get_fhog,'fparams',hog_params),...
    struct('getFeature',@get_table_feature, 'fparams',cn_params),...
};
%  Global feature parameters
params.t_global.cell_size = 4;                  % Feature cell size

%   Search region + extended background parameters
params.search_area_shape = 'square';    % the shape of the training/detection window: 'proportional', 'square' or 'fix_padding'
params.search_area_scale =5;           % the size of the training/detection area proportional to the target size
params.image_sample_size = 160^2;   % Minimum area of image samples

%   Gaussian response parameter
params.output_sigma_factor =0.051;		% standard deviation of the desired correlation output (proportional to target)

%   Detection parameters
params.newton_iterations  = 5;           % number of Newton's iteration to maximize the detection scores

%  Set files and gt
params.name=seq.video_name;
params.video_path = seq.video_path;
params.img_files = seq.s_frames;
params.wsize    = [seq.init_rect(1,4), seq.init_rect(1,3)];
params.init_pos = [seq.init_rect(1,2), seq.init_rect(1,1)] + floor(params.wsize/2);
params.s_frames = seq.s_frames;
params.no_fram  = seq.en_frame - seq.st_frame + 1;
params.seq_st_frame = seq.st_frame;
params.seq_en_frame = seq.en_frame;
params.ground_truth=seq.init_rect;
params.beta=0.2;
%   ADMM parameters, # of iteration, and lambda- mu and betha are set in
%   the main function.
params.admm_iterations1 = 3;
params.admm_iterations2 = 3;
params.admm_lambda1 =0.01;
params.admm_lambda2 =0.01;
params.admm_mu=1;
params.admm_mu_m=1;
params.eta=180;
params.weight=0.01;

params.reg_window_max=1e5;
params.reg_window_min=1e-3;

%  Scale parameters
params.scale_sigma_factor=0.51;   
params.num_scales=33;
params.scale_step=1.03;
params.scale_model_factor = 1.0;
params.scale_model_max_area = 32*16;
params.hog_scale_cell_size = 4;  
params.scale_lambda = 1e-4;      
params.learning_rate_scale=0.023;
params.learning_rate = 0.024;
params.ratio=0.75;

%   Debug and visualization
params.visualization =0;

params.is_dark=illu_judge(params);
% fprintf(['Is dark:' num2str(params.is_dark)]);
%   Run the main function
if params.is_dark
    results = tracker(params);
else
    params.eta=280;
    params.weight=0.02;
    params.learning_rate_scale=0.016;
    params.learning_rate = 0.032;
    params.ratio=0.76;
    params.output_sigma_factor =0.058;
    params.scale_sigma_factor=0.5;   
    results = tracker(params);
end