diff --git a/frigate/test/test_obects.py b/frigate/test/test_obects.py
index 8fe831980e..ee0162ec01 100644
--- a/frigate/test/test_obects.py
+++ b/frigate/test/test_obects.py
@@ -1,6 +1,22 @@
+import random
 import unittest
 
+import numpy as np
+
 from frigate.track.tracked_object import TrackedObjectAttribute
+from frigate.util.object import average_boxes
+
+
+class TestBoxStatistics(unittest.TestCase):
+    def test_average_boxes_matches_numpy(self) -> None:
+        rng = random.Random(0)
+        for _ in range(5000):
+            boxes = [
+                [rng.randint(0, 4000) for _ in range(4)]
+                for _ in range(rng.randint(1, 10))
+            ]
+            expected = [float(np.mean([b[i] for b in boxes])) for i in range(4)]
+            self.assertEqual(average_boxes(boxes), expected)
 
 
 class TestAttribute(unittest.TestCase):
diff --git a/frigate/util/object.py b/frigate/util/object.py
index 311fccf477..b4ee72dc07 100644
--- a/frigate/util/object.py
+++ b/frigate/util/object.py
@@ -339,18 +339,13 @@ def reduce_boxes(boxes, iou_threshold=0.0):
 
 def average_boxes(boxes: list[list[int, int, int, int]]) -> list[int, int, int, int]:
     """Return a box that is the average of a list of boxes."""
-    x_mins = []
-    y_mins = []
-    x_max = []
-    y_max = []
-
-    for box in boxes:
-        x_mins.append(box[0])
-        y_mins.append(box[1])
-        x_max.append(box[2])
-        y_max.append(box[3])
-
-    return [np.mean(x_mins), np.mean(y_mins), np.mean(x_max), np.mean(y_max)]
+    n = len(boxes)
+    return [
+        sum(box[0] for box in boxes) / n,
+        sum(box[1] for box in boxes) / n,
+        sum(box[2] for box in boxes) / n,
+        sum(box[3] for box in boxes) / n,
+    ]
 
 
 def median_of_boxes(boxes: list[list[int, int, int, int]]) -> list[int, int, int, int]: