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MDP_train_det.m
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% --------------------------------------------------------
% MDP Tracking
% Copyright (c) 2015 CVGL Stanford
% Licensed under The MIT License [see LICENSE for details]
% Written by Yu Xiang
% --------------------------------------------------------
%
% offline training using object detections
function tracker = MDP_train_det(seq_idx, tracker)
is_show = 0;
is_save = 1;
is_text = 0;
is_pause = 0;
opt = globals();
opt.is_show = is_show;
seq_name = opt.mot2d_train_seqs{seq_idx};
seq_num = opt.mot2d_train_nums(seq_idx);
seq_set = 'train';
if is_show
close all;
end
% build the dres structure for images
filename = sprintf('%s/%s_dres_image.mat', opt.results, seq_name);
if exist(filename, 'file') ~= 0
object = load(filename);
dres_image = object.dres_image;
fprintf('load images from file %s done\n', filename);
else
dres_image = read_dres_image(opt, seq_set, seq_name, seq_num);
fprintf('read images done\n');
save(filename, 'dres_image', '-v7.3');
end
% generate training data
I = dres_image.Igray{1};
[dres_train, dres_det, labels] = generate_training_data(seq_idx, dres_image, opt);
% for debugging
% dres_train = {dres_train{6}};
% intialize tracker
if nargin < 2 || isempty(tracker) == 1
fprintf('initialize tracker from scratch\n');
tracker = MDP_initialize(I, dres_det, labels, opt);
else
% continuous training
fprintf('continuous training\n');
tracker.image_width = size(I,2);
tracker.image_height = size(I,1);
tracker.max_width = max(dres_det.w);
tracker.max_height = max(dres_det.h);
tracker.max_score = max(dres_det.r);
factive = MDP_feature_active(tracker, dres_det);
index = labels ~= 0;
tracker.factive = [tracker.factive; factive(index,:)];
tracker.lactive = [tracker.lactive; labels(index)];
tracker.w_active = svmtrain(tracker.lactive, tracker.factive, '-c 1 -q');
end
% for each training sequence
num_train = numel(dres_train);
for t = 1:num_train
fprintf('tracking sequence %d\n', t);
dres_gt = dres_train{t};
% first frame
fr = dres_gt.fr(1);
id = dres_gt.id(1);
% reset tracker
tracker.prev_state = 1;
tracker.state = 1;
tracker.target_id = id;
% start tracking
while fr <= dres_gt.fr(end)
if is_text
fprintf('\nframe %d, state %d\n', fr, tracker.state);
end
% extract detection
index = find(dres_det.fr == fr);
dres = sub(dres_det, index);
num_det = numel(dres.fr);
% show results
if is_show
figure(1);
% show ground truth
subplot(2, 3, 1);
show_dres(fr, dres_image.I{fr}, 'GT', dres_gt);
% show detections
subplot(2, 3, 2);
show_dres(fr, dres_image.I{fr}, 'Detections', dres_det);
end
if tracker.state == 0
break;
elseif tracker.state == 1
% compute overlap
overlap = calc_overlap(dres_gt, 1, dres, 1:num_det);
[ov, ind] = max(overlap);
if is_text
fprintf('Start: first frame overlap %.2f\n', ov);
end
% initialize the LK tracker
tracker = LK_initialize(tracker, fr, id, dres, ind, dres_image);
tracker.state = 2;
tracker.streak_occluded = 0;
% build the dres structure
dres_one = sub(dres, ind);
tracker.dres = dres_one;
tracker.dres.id = tracker.target_id;
tracker.dres.state = tracker.state;
% tracking by association
else
% find object detections for association
dres = MDP_crop_image_box(dres, dres_image.Igray{fr}, tracker);
[dres, index_det, ctrack] = generate_association_index(tracker, fr, dres);
index_gt = find(dres_gt.fr == fr);
if dres_gt.covered(index_gt) ~= 0
index_det = [];
end
if is_show
figure(1);
subplot(2, 3, 3);
show_dres(fr, dres_image.I{fr}, 'Potential Associations', sub(dres, index_det));
hold on;
plot(ctrack(1), ctrack(2), 'ro', 'LineWidth', 2);
hold off;
end
% compute features
if isempty(index_det) == 0
% extract features with LK association
dres_associate = sub(dres, index_det);
features = MDP_feature_occluded(fr, dres_image, dres_associate, tracker);
% compute labels
flag_update = 0;
m = size(features, 1);
labels = -1 * ones(m, 1);
% compute overlap
if isempty(index_gt) == 0
overlap = calc_overlap(dres_gt, index_gt, dres_associate, 1:numel(index_det));
[ov, ind] = max(overlap);
if ov > opt.overlap_pos
labels(ind) = 1;
flag_update = 1;
end
end
% update features
tracker.f_occluded(end+1:end+m,:) = features;
tracker.l_occluded(end+1:end+m) = labels;
if is_text
fprintf('training examples in occluded state %d\n', size(tracker.f_occluded,1));
end
% update template
if flag_update
dres_one = sub(dres_associate, ind);
tracker = LK_associate(fr, dres_image, dres_one, tracker);
tracker.prev_state = tracker.state;
tracker.state = 2;
% build the dres structure
dres_one = [];
dres_one.fr = fr;
dres_one.id = tracker.target_id;
dres_one.x = tracker.bb(1);
dres_one.y = tracker.bb(2);
dres_one.w = tracker.bb(3) - tracker.bb(1);
dres_one.h = tracker.bb(4) - tracker.bb(2);
dres_one.r = 1;
dres_one.state = 2;
if tracker.dres.fr(end) == fr
dres_tmp = tracker.dres;
index_tmp = 1:numel(dres_tmp.fr)-1;
tracker.dres = sub(dres_tmp, index_tmp);
end
tracker.dres = interpolate_dres(tracker.dres, dres_one);
% update LK tracker
tracker = LK_update(fr, tracker, dres_image.Igray{fr}, dres_associate, 1);
else
tracker.state = 3;
dres_one = sub(tracker.dres, numel(tracker.dres.fr));
dres_one.fr = fr;
dres_one.id = tracker.target_id;
dres_one.state = 3;
if tracker.dres.fr(end) == fr
dres_tmp = tracker.dres;
index_tmp = 1:numel(dres_tmp.fr)-1;
tracker.dres = sub(dres_tmp, index_tmp);
end
tracker.dres = concatenate_dres(tracker.dres, dres_one);
end
else
tracker.state = 3;
dres_one = sub(tracker.dres, numel(tracker.dres.fr));
dres_one.fr = fr;
dres_one.id = tracker.target_id;
dres_one.state = 3;
if tracker.dres.fr(end) == fr
dres_tmp = tracker.dres;
index_tmp = 1:numel(dres_tmp.fr)-1;
tracker.dres = sub(dres_tmp, index_tmp);
end
tracker.dres = concatenate_dres(tracker.dres, dres_one);
end
end
% show results
if is_show
figure(1);
% show tracking results
subplot(2, 3, 4);
show_dres(fr, dres_image.I{fr}, 'Tracking', tracker.dres, 2);
% show lost targets
subplot(2, 3, 5);
show_dres(fr, dres_image.I{fr}, 'Lost', tracker.dres, 3);
subplot(2, 3, 6);
show_templates(tracker, dres_image);
fprintf('frame %d, state %d\n', fr, tracker.state);
if is_pause
pause();
else
pause(0.01);
end
% filename = sprintf('results/%s_%06d.png', seq_name, fr);
% hgexport(h, filename, hgexport('factorystyle'), 'Format', 'png');
end
fr = fr + 1;
end
end
tracker.w_occluded = svmtrain(tracker.l_occluded, tracker.f_occluded, '-c 1 -q -g 1 -b 1');
fprintf('Finish training %s\n', seq_name);
% save model
if is_save
filename = sprintf('%s/%s_tracker_det.mat', opt.results, seq_name);
save(filename, 'tracker');
end