diff --git a/config/config.example.yml b/config/config.example.yml
index bd34db352..8d81678cb 100644
--- a/config/config.example.yml
+++ b/config/config.example.yml
@@ -76,39 +76,6 @@ objects:
       min_score: 0.5
       threshold: 0.85
 
-zones:
-  #################
-  # Name of the zone
-  ################
-  front_steps:
-    front_door:
-      ####################
-      # For each camera, a list of x,y coordinates to define the polygon of the zone. The top 
-      # left corner is 0,0. Can also be a comma separated string of all x,y coordinates combined.
-      # The same zone can exist across multiple cameras if they have overlapping FOVs.
-      # An object is determined to be in the zone based on whether or not the bottom center
-      # of it's bounding box is within the polygon. The polygon must have at least 3 points.
-      # Coordinates can be generated at https://www.image-map.net/
-      ####################
-      coordinates:
-        - 545,1077
-        - 747,939
-        - 788,805
-      ################
-      # Zone level object filters. These are applied in addition to the global and camera filters
-      # and should be more restrictive than the global and camera filters. The global and camera
-      # filters are applied upstream.
-      ################
-      filters:
-        person:
-          min_area: 5000
-          max_area: 100000
-          threshold: 0.8
-  driveway:
-    front_door:
-      coordinates: 545,1077,747,939,788,805
-  yard:
-
 cameras:
   back:
     ffmpeg:
@@ -169,6 +136,37 @@ cameras:
     #   crop_to_region: True
     #   snapshot_height: 300
 
+    ################
+    # Zones
+    ################
+    zones:
+      #################
+      # Name of the zone
+      ################
+      front_steps:
+        ####################
+        # A list of x,y coordinates to define the polygon of the zone. The top 
+        # left corner is 0,0. Can also be a comma separated string of all x,y coordinates combined.
+        # The same zone name can exist across multiple cameras if they have overlapping FOVs.
+        # An object is determined to be in the zone based on whether or not the bottom center
+        # of it's bounding box is within the polygon. The polygon must have at least 3 points.
+        # Coordinates can be generated at https://www.image-map.net/
+        ####################
+        coordinates:
+          - 545,1077
+          - 747,939
+          - 788,805
+        ################
+        # Zone level object filters. These are applied in addition to the global and camera filters
+        # and should be more restrictive than the global and camera filters. The global and camera
+        # filters are applied upstream.
+        ################
+        filters:
+          person:
+            min_area: 5000
+            max_area: 100000
+            threshold: 0.8
+
     ################
     # This will save a clip for each tracked object by frigate along with a json file that contains
     # data related to the tracked object. This works by telling ffmpeg to write video segments to /cache
diff --git a/detect_objects.py b/detect_objects.py
index b84e92de6..eaf20202c 100644
--- a/detect_objects.py
+++ b/detect_objects.py
@@ -182,7 +182,7 @@ def main():
             'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True),
             'draw_zones': config.get('snapshots', {}).get('draw_zones', False)
         }
-        config['zones'] = {}
+        config['zones'] = config.get('zones', {})
 
     # Queue for cameras to push tracked objects to
     tracked_objects_queue = mp.Queue()
@@ -293,7 +293,7 @@ def main():
     event_processor = EventProcessor(CONFIG['cameras'], camera_processes, '/cache', '/clips', event_queue, stop_event)
     event_processor.start()
     
-    object_processor = TrackedObjectProcessor(CONFIG['cameras'], CONFIG.get('zones', {}), client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue, stop_event)
+    object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue, stop_event)
     object_processor.start()
     
     camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, plasma_process, stop_event)
diff --git a/frigate/object_processing.py b/frigate/object_processing.py
index ee2671c6b..9a99dd5a7 100644
--- a/frigate/object_processing.py
+++ b/frigate/object_processing.py
@@ -230,10 +230,9 @@ class CameraState():
 
 
 class TrackedObjectProcessor(threading.Thread):
-    def __init__(self, camera_config, zone_config, client, topic_prefix, tracked_objects_queue, event_queue, stop_event):
+    def __init__(self, camera_config, client, topic_prefix, tracked_objects_queue, event_queue, stop_event):
         threading.Thread.__init__(self)
         self.camera_config = camera_config
-        self.zone_config = zone_config
         self.client = client
         self.topic_prefix = topic_prefix
         self.tracked_objects_queue = tracked_objects_queue
@@ -299,25 +298,24 @@ class TrackedObjectProcessor(threading.Thread):
         self.zone_data = defaultdict(lambda: defaultdict(lambda: set()))
 
         # set colors for zones
+        all_zone_names = set([zone for config in self.camera_config.values() for zone in config['zones'].keys()])
         zone_colors = {}
-        colors = plt.cm.get_cmap('tab10', len(self.zone_config.keys()))
-        for i, zone in enumerate(self.zone_config.keys()):
+        colors = plt.cm.get_cmap('tab10', len(all_zone_names))
+        for i, zone in enumerate(all_zone_names):
             zone_colors[zone] = tuple(int(round(255 * c)) for c in colors(i)[:3])
 
         # create zone contours
-        for zone_name, config in zone_config.items():
-            for camera, camera_zone_config in config.items():
-                camera_zone = {}
-                camera_zone['color'] = zone_colors[zone_name]
-                coordinates = camera_zone_config['coordinates']
+        for camera_config in self.camera_config.values():
+            for zone_name, zone_config in camera_config['zones'].items():
+                zone_config['color'] = zone_colors[zone_name]
+                coordinates = zone_config['coordinates']
                 if isinstance(coordinates, list):
-                    camera_zone['contour'] =  np.array([[int(p.split(',')[0]), int(p.split(',')[1])] for p in coordinates])
+                    zone_config['contour'] =  np.array([[int(p.split(',')[0]), int(p.split(',')[1])] for p in coordinates])
                 elif isinstance(coordinates, str):
                     points = coordinates.split(',')
-                    camera_zone['contour'] =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
+                    zone_config['contour'] =  np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
                 else:
                     print(f"Unable to parse zone coordinates for {zone_name} - {camera}")
-                self.camera_config[camera]['zones'][zone_name] = camera_zone
         
     def get_best(self, camera, label):
         best_objects = self.camera_states[camera].best_objects