diff --git a/winnow/executor.py b/winnow/executor.py
index c8461b5..ab02eef 100644
--- a/winnow/executor.py
+++ b/winnow/executor.py
@@ -600,7 +600,10 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                     )
 
             finally:
-                flush_batch(UPLOAD_TRACKER_FILE)
+                try:
+                    flush_batch(UPLOAD_TRACKER_FILE)
+                except Exception as _flush_exc:
+                    logger.warning("flush_batch failed during cleanup — batch will be recovered on next begin_batch: %s", _flush_exc)
 
             # Batch-map Frigate filenames to asset IDs now that all uploads are done.
             if actually_uploaded and not _skip_reconcile:
diff --git a/winnow/quality.py b/winnow/quality.py
index bd78d7a..1becded 100644
--- a/winnow/quality.py
+++ b/winnow/quality.py
@@ -14,6 +14,11 @@ from PIL import Image
 logger = logging.getLogger(__name__)
 
 
+def _laplacian_var(img_np: np.ndarray) -> float:
+    gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) if img_np.ndim == 3 else img_np
+    return float(cv2.Laplacian(gray, cv2.CV_64F).var())
+
+
 @dataclass
 class QualityResult:
     """Result of quality assessment on a face/image crop."""
@@ -32,8 +37,7 @@ def check_blur(img_np: np.ndarray, threshold: float = 100.0) -> tuple[bool, str]
 
     Lower variance = blurrier image. ArcFace needs clear facial features.
     """
-    gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) if img_np.ndim == 3 else img_np
-    variance = cv2.Laplacian(gray, cv2.CV_64F).var()
+    variance = _laplacian_var(img_np)
     if variance < threshold:
         return False, f"Blurry (laplacian={variance:.1f}, threshold={threshold})"
     return True, ""
@@ -115,8 +119,7 @@ def assess_quality(
     reasons = []
 
     # Compute laplacian variance once (used by check_blur and stored as blur_score)
-    gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) if img_np.ndim == 3 else img_np
-    blur_score = float(cv2.Laplacian(gray, cv2.CV_64F).var())
+    blur_score = _laplacian_var(img_np)
 
     checks = [
         (
@@ -154,8 +157,7 @@ def blur_score_from_image(img: Image.Image, max_dim: int = 1440) -> float | None
         if score_img.width > max_dim or score_img.height > max_dim:
             score_img = score_img.copy()
             score_img.thumbnail((max_dim, max_dim), Image.LANCZOS)
-        gray = cv2.cvtColor(np.array(score_img), cv2.COLOR_RGB2GRAY)
-        return float(cv2.Laplacian(gray, cv2.CV_64F).var())
+        return _laplacian_var(np.array(score_img))
     except Exception as exc:
         logger.debug("blur_score_from_image failed: %s", exc)
         return None
diff --git a/winnow/upload_tracker.py b/winnow/upload_tracker.py
index e29f440..69d741a 100644
--- a/winnow/upload_tracker.py
+++ b/winnow/upload_tracker.py
@@ -281,9 +281,11 @@ def get_tracked_frigate_filenames(person_name: str) -> set[str]:
 def has_frigate_scores(person_name: str) -> bool:
     """Return True if any mapped file for this person has a stored Frigate recognition score."""
     data = _load(UPLOAD_TRACKER_FILE)
-    entry = _migrate_entry(data.get("by_person", {}).get(person_name, {}))
-    frigate_files = entry.get("frigate_files", {})
-    frigate_scores = entry.get("frigate_scores", {})
+    raw = data.get("by_person", {}).get(person_name)
+    if not raw or isinstance(raw, list):
+        return False
+    frigate_files = raw.get("frigate_files", {})
+    frigate_scores = raw.get("frigate_scores", {})
     return any(asset_id in frigate_scores for asset_id in frigate_files.values())