def METHOD_NAME(detections, trackers, iou_threshold):
    if (len(trackers) == 0):
        return np.empty(
            (0, 2), dtype=int), np.arange(len(detections)), np.empty(
                (0, 5), dtype=int)
    iou_matrix = iou_batch(detections, trackers)
    if min(iou_matrix.shape) > 0:
        a = (iou_matrix > iou_threshold).astype(np.int32)
        if a.sum(1).max() == 1 and a.sum(0).max() == 1:
            matched_indices = np.stack(np.where(a), axis=1)
        else:
            matched_indices = linear_assignment(-iou_matrix)
    else:
        matched_indices = np.empty(shape=(0, 2))
    unmatched_detections = []
    for d, det in enumerate(detections):
        if (d not in matched_indices[:, 0]):
            unmatched_detections.append(d)
    unmatched_trackers = []
    for t, trk in enumerate(trackers):
        if (t not in matched_indices[:, 1]):
            unmatched_trackers.append(t)
    # filter out matched with low IOU
    matches = []
    for m in matched_indices:
        if (iou_matrix[m[0], m[1]] < iou_threshold):
            unmatched_detections.append(m[0])
            unmatched_trackers.append(m[1])
        else:
            matches.append(m.reshape(1, 2))
    if (len(matches) == 0):
        matches = np.empty((0, 2), dtype=int)
    else:
        matches = np.concatenate(matches, axis=0)
    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)