diff --git a/src/pyrecest/evaluation/tracking_metrics/_clear_identity.py b/src/pyrecest/evaluation/tracking_metrics/_clear_identity.py
index 46f898a67..e4debd70d 100644
--- a/src/pyrecest/evaluation/tracking_metrics/_clear_identity.py
+++ b/src/pyrecest/evaluation/tracking_metrics/_clear_identity.py
@@ -46,9 +46,11 @@ def evaluate_clear(data: TrackingSequence, *, threshold: float) -> ClearCounts:
     ):
         if len(gt_ids) == 0:
             fp += len(tracker_ids)
+            previous_timestep_id[:] = np.nan
             continue
         if len(tracker_ids) == 0:
             fn += len(gt_ids)
+            previous_timestep_id[:] = np.nan
             continue
         continuity = tracker_ids[np.newaxis, :] == previous_timestep_id[gt_ids[:, None]]
         score = continuity.astype(float) * 1000.0 + similarity
diff --git a/tests/evaluation/test_tracking_metrics_clear_gap.py b/tests/evaluation/test_tracking_metrics_clear_gap.py
new file mode 100644
index 000000000..578472b99
--- /dev/null
+++ b/tests/evaluation/test_tracking_metrics_clear_gap.py
@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from pyrecest.evaluation.tracking_metrics import TrackingSequence, evaluate_clear
+
+
+@pytest.mark.parametrize(
+    ("gap_gt_ids", "gap_tracker_ids", "gap_similarity", "expected_fp", "expected_fn"),
+    [
+        (
+            np.empty(0, dtype=int),
+            np.array([0], dtype=int),
+            np.empty((0, 1), dtype=float),
+            1,
+            0,
+        ),
+        (
+            np.array([0, 1], dtype=int),
+            np.empty(0, dtype=int),
+            np.empty((2, 0), dtype=float),
+            0,
+            2,
+        ),
+    ],
+)
+def test_clear_continuity_does_not_cross_empty_frame(
+    gap_gt_ids: np.ndarray,
+    gap_tracker_ids: np.ndarray,
+    gap_similarity: np.ndarray,
+    expected_fp: int,
+    expected_fn: int,
+) -> None:
+    data = TrackingSequence(
+        gt_ids=(
+            np.array([0, 1], dtype=int),
+            gap_gt_ids,
+            np.array([0, 1], dtype=int),
+        ),
+        tracker_ids=(
+            np.array([0, 1], dtype=int),
+            gap_tracker_ids,
+            np.array([0, 1], dtype=int),
+        ),
+        similarity_scores=(
+            np.eye(2, dtype=float),
+            gap_similarity,
+            np.array([[0.6, 0.9], [0.9, 0.6]], dtype=float),
+        ),
+        num_gt_ids=2,
+        num_tracker_ids=2,
+    )
+
+    counts = evaluate_clear(data, threshold=0.5)
+
+    assert counts.tp == 4
+    assert counts.fp == expected_fp
+    assert counts.fn == expected_fn
+    assert counts.id_switches == 2
+    assert counts.motp_sum == pytest.approx(3.8)