diff --git a/docs/docs/configuration/index.md b/docs/docs/configuration/index.md
index c74098110..826c64523 100644
--- a/docs/docs/configuration/index.md
+++ b/docs/docs/configuration/index.md
@@ -211,7 +211,7 @@ motion:
   # Low values will cause things like moving shadows to be detected as motion for longer.
   # https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
   frame_alpha: 0.2
-  # Optional: Height of the resized motion frame  (default: 80)
+  # Optional: Height of the resized motion frame  (default: 50)
   # This operates as an efficient blur alternative. Higher values will result in more granular motion detection at the expense
   # of higher CPU usage. Lower values result in less CPU, but small changes may not register as motion.
   frame_height: 50
diff --git a/frigate/video.py b/frigate/video.py
index 7f3659144..449f9570a 100755
--- a/frigate/video.py
+++ b/frigate/video.py
@@ -75,7 +75,25 @@ def filtered(obj, objects_to_track, object_filters):
 
 
 def create_tensor_input(frame, model_shape, region):
-    cropped_frame = yuv_region_2_rgb(frame, region)
+    # TODO: is it faster to just convert grayscale to RGB? or repeat dimensions with numpy?
+    height = frame.shape[0] // 3 * 2
+    width = frame.shape[1]
+
+    # get the crop box if the region extends beyond the frame
+    crop_x1 = max(0, region[0])
+    crop_y1 = max(0, region[1])
+    crop_x2 = min(width, region[2])
+    crop_y2 = min(height, region[3])
+
+    size = region[3] - region[1]
+    cropped_frame = np.zeros((size, size), np.uint8)
+
+    cropped_frame[
+        0 : crop_y2 - crop_y1,
+        0 : crop_x2 - crop_x1,
+    ] = frame[crop_y1:crop_y2, crop_x1:crop_x2]
+
+    cropped_frame = np.repeat(np.expand_dims(cropped_frame, -1), 3, 2)
 
     # Resize to 300x300 if needed
     if cropped_frame.shape != (model_shape[0], model_shape[1], 3):