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# Source: https://github.com/bochinski/iou-tracker
from .utils import iou
def track_iou(detections, sigma_l, sigma_h, sigma_iou, t_min):
"""
Simple IOU based tracker.
See "High-Speed Tracking-by-Detection Without Using Image Information by E. Bochinski, V. Eiselein, T. Sikora" for
more information.
Args:
detections (list): list of detections per frame, usually generated by util.load_mot
sigma_l (float): low detection threshold.
sigma_h (float): high detection threshold.
sigma_iou (float): IOU threshold.
t_min (float): minimum track length in frames.
Returns:
list: list of tracks.
"""
tracks_active = []
tracks_finished = []
for frame_num, detections_frame in enumerate(detections, start=1):
# apply low threshold to detections
dets = [det for det in detections_frame if det['score'] >= sigma_l]
updated_tracks = []
for track in tracks_active:
if len(dets) > 0:
# get det with highest iou
best_match = max(dets, key=lambda x: iou(track['bboxes'][-1], x['bbox']))
if iou(track['bboxes'][-1], best_match['bbox']) >= sigma_iou:
track['bboxes'].append(best_match['bbox'])
track['max_score'] = max(track['max_score'], best_match['score'])
updated_tracks.append(track)
# remove from best matching detection from detections
del dets[dets.index(best_match)]
# if track was not updated
if len(updated_tracks) == 0 or track is not updated_tracks[-1]:
# finish track when the conditions are met
if track['max_score'] >= sigma_h and len(track['bboxes']) >= t_min:
tracks_finished.append(track)
# create new tracks
new_tracks = [{'bboxes': [det['bbox']], 'max_score': det['score'], 'start_frame': frame_num} for det in dets]
tracks_active = updated_tracks + new_tracks
# finish all remaining active tracks
tracks_finished += [track for track in tracks_active
if track['max_score'] >= sigma_h and len(track['bboxes']) >= t_min]
return tracks_finished
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