def min_cost_matching(distance_metric, max_distance, tracks, detections,
                      track_indices=None, detection_indices=None):
    if track_indices is None:
        track_indices = np.arange(len(tracks))
    if detection_indices is None:
        detection_indices = np.arange(len(detections))

    if len(detection_indices) == 0 or len(track_indices) == 0:
        return [], track_indices, detection_indices  # Nothing to match.

    # 计算成本矩阵
    cost_matrix = distance_metric(
        tracks, detections, track_indices, detection_indices)
    cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5

    # 执行匈牙利算法，得到指派成功的索引对，行索引为tracks的索引，列索引为detections的索引
    row_indices, col_indices = linear_assignment(cost_matrix)

    matches, unmatched_tracks, unmatched_detections = [], [], []
    # 找出未匹配的detections
    for col, detection_idx in enumerate(detection_indices):
        if col not in col_indices:
            unmatched_detections.append(detection_idx)
    # 找出未匹配的tracks
    for row, track_idx in enumerate(track_indices):
        if row not in row_indices:
            unmatched_tracks.append(track_idx)
    # 遍历匹配的(track, detection)索引对
    for row, col in zip(row_indices, col_indices):
        track_idx = track_indices[row]
        detection_idx = detection_indices[col]
        # 如果相应的cost大于阈值max_distance，也视为未匹配成功
        if cost_matrix[row, col] > max_distance:
            unmatched_tracks.append(track_idx)
            unmatched_detections.append(detection_idx)
        else:
            matches.append((track_idx, detection_idx))
    return matches, unmatched_tracks, unmatched_detections

# Associate remaining tracks together with unconfirmed tracks using IOU.        
# 将 不确定态的轨迹 和 级联匹配中刚刚没有匹配上的轨迹 组合为 iou匹配候选集合 iou_track_candidates
iou_track_candidates = unconfirmed_tracks + [k for k in unmatched_tracks_a if tracks[k].time_since_update == 1]
# 级联匹配中并非刚刚没有匹配上的轨迹
unmatched_tracks_a = [k for k in unmatched_tracks_a if tracks[k].time_since_update != 1]

# 对级联匹配中还没有匹配成功的目标, 使用匈牙利算法进行IoU匹配
matches_b, unmatched_tracks_b, unmatched_detections = linear_assignment.min_cost_matching(
    iou_matching.iou_cost, max_iou_distance, tracks,
    detections, iou_track_candidates, unmatched_detections)