tracker added

Author: bochinski

iou_tracker.py

@@ -0,0 +1,103 @@
+# ---------------------------------------------------------
+# IOU Tracker
+# Copyright (c) 2017 TU Berlin, Communication Systems Group
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Erik Bochinski
+# ---------------------------------------------------------
+
+from time import time
+
+from util import load_mot, save_to_csv, 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:
+            # matches = [det for det in dets if track.get_iou(det['bbox']) >= min_iou]
+            # high_matches = [match for match in matches if match['score'] >= thresh_high]
+            high_matches = [det for det in dets if iou(track['bboxes'][-1], det['bbox']) >= sigma_iou]
+
+            if len(high_matches) > 0:
+                # extract best matching detection
+                best_match = max(high_matches, key=lambda x: iou(track['bboxes'][-1], x['bbox']))
+                track['bboxes'].append(best_match['bbox'])
+                track['max_score'] = max(track['max_score'], best_match['score'])
+
+                updated_tracks.append(track)
+
+                # remove from detections
+                del dets[dets.index(best_match)]
+
+            else:
+                # finish track when there is no suitable match
+                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
+
+
+def track_iou_matlab_wrapper(detections, sigma_l, sigma_h, sigma_iou, t_min):
+    """
+    Matlab wrapper of the iou tracker for the detrac evaluation toolkit.
+
+    Args:
+         detections (numpy.array): numpy array of detections, usually supplied by run_tracker.m
+         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:
+        float: speed in frames per second.
+        list: list of tracks.
+    """
+
+    detections = detections.reshape((7, -1)).transpose()
+    dets = load_mot(detections)
+    start = time()
+    tracks = track_iou(dets, sigma_l, sigma_h, sigma_iou, t_min)
+    end = time()
+
+    id_ = 1
+    out = []
+    for track in tracks:
+        for i, bbox in enumerate(track['bboxes']):
+            out += [float(bbox[0]), float(bbox[1]), float(bbox[2] - bbox[0]), float(bbox[3] - bbox[1]),
+                    float(track['start_frame'] + i), float(id_)]
+        id_ += 1
+
+    num_frames = len(dets)
+    speed = num_frames / (end - start)
+
+    return speed, out