run_tracker.m

%
%  Long-term Correlation Tracking v2.0
%  
%  Chao Ma, 2015
%  https://sites.google.com/site/chaoma99/
%
%  Note that in this updated version, an online SVM classifier is used for 
%  recovering targets and the color channels are quantized as features for
%  detector learning


function [precision, fps] = run_tracker(video, show_visualization, show_plots)

    addpath('utility');
    addpath('detector');
    addpath('scale');
    
	%path to the videos (you'll be able to choose one with the GUI).
	base_path = 'D:/dataset/Benchmark/';
	%default settings
	if nargin < 1, video = 'choose'; end
	if nargin < 2, show_visualization = ~strcmp(video, 'all'); end
	if nargin < 3, show_plots = ~strcmp(video, 'all'); end

	
	config.padding = 1.8;  %extra area surrounding the target
    
	config.lambda = 1e-4;  %regularization
	config.output_sigma_factor = 0.1;  %spatial bandwidth (proportional to target)	        
    config.interp_factor=0.01; % best 0.01
    config.kernel_sigma = 1;

    config.features.hog_orientations = 9;
    config.features.cell_size = 4;   % size of hog grid cell		
    config.features.window_size = 6; % size of local region for intensity historgram  
    config.features.nbins=8; % bins of intensity historgram
    
    config.motion_thresh=0.15;  
%     config.appearance_thresh=0.38;
    config.appearance_thresh=0.38;
	
    switch video
	case 'choose',
		%ask the user for the video, then call self with that video name.
		video = choose_video(base_path);
		if ~isempty(video),
			[precision, fps] = run_tracker(video,show_visualization, show_plots);
            
			if nargout == 0,  %don't output precision as an argument
				clear precision
			end
		end
				
	case 'all',
		%all videos, call self with each video name.
		
		%only keep valid directory names
		dirs = dir(base_path);
		videos = {dirs.name};
		videos(strcmp('.', videos) | strcmp('..', videos) | ...
			strcmp('anno', videos) | ~[dirs.isdir]) = [];
		
		%the 'Jogging' sequence has 2 targets, create one entry for each.
		%we could make this more general if multiple targets per video
		%becomes a common occurence.
		videos(strcmpi('Jogging', videos)) = [];
		videos(end+1:end+2) = {'Jogging.1', 'Jogging.2'};
		
		all_precisions = zeros(numel(videos),1);  %to compute averages
		all_fps = zeros(numel(videos),1);
		
		if ~exist('matlabpool', 'file'),
			%no parallel toolbox, use a simple 'for' to iterate
			for k = 1:numel(videos),
				[all_precisions(k), all_fps(k)] = run_tracker(videos{k}, show_visualization, show_plots);
			end
		else
			%evaluate trackers for all videos in parallel
			if matlabpool('size') == 0,
				matlabpool open;
			end
			parfor k = 1:numel(videos),
				[all_precisions(k), all_fps(k)] = run_tracker(videos{k}, show_visualization, show_plots);
			end
		end
		
		%compute average precision at 20px, and FPS
		mean_precision = mean(all_precisions);
		fps = mean(all_fps);
		fprintf('\nAverage precision (20px):% 1.3f, Average FPS:% 4.2f\n\n', mean_precision, fps)
		if nargout > 0,
			precision = mean_precision;
        end
		
		
	otherwise
		%we were given the name of a single video to process.
	
		%get image file names, initial state, and ground truth for evaluation
		[img_files, pos, target_sz, ground_truth, video_path] = load_video_info(base_path, video);
		
        
		%call tracker function with all the relevant parameters
		[positions, time] = tracker_lct(video_path, img_files, pos, target_sz, config, show_visualization);
		
		
		%calculate and show precision plot, as well as frames-per-second
		precisions = precision_plot(positions, ground_truth, video, show_plots);
		fps = numel(img_files) / time;

		fprintf('%12s - Precision (20px):% 1.3f, FPS:% 4.2f\n', video, precisions(20), fps)

		if nargout > 0,
			%return precisions at a 20 pixels threshold
			precision = precisions(20);
        end
        
	end
end