update config example

config/config.example.yml

@@ -47,16 +47,24 @@ mqtt:
 #     - rgb24
 
 ####################
-# Global object configuration. Applies to all cameras and regions
-# unless overridden at the camera/region levels.
+# Global object configuration. Applies to all cameras
+# unless overridden at the camera levels.
 # Keys must be valid labels. By default, the model uses coco (https://dl.google.com/coral/canned_models/coco_labels.txt).
 # All labels from the model are reported over MQTT. These values are used to filter out false positives.
+# min_area (optional): minimum width*height of the bounding box for the detected person
+# max_area (optional): maximum width*height of the bounding box for the detected person
+# threshold (optional): The minimum decimal percentage (50% hit = 0.5) for the confidence from tensorflow
 ####################
 objects:
-  person:
-    min_area: 5000
-    max_area: 100000
-    threshold: 0.5
+  track:
+    - person
+    - car
+    - truck
+  filters:
+    person:
+      min_area: 5000
+      max_area: 100000
+      threshold: 0.5
 
 cameras:
   back:
@@ -91,18 +99,21 @@ cameras:
     ################
     take_frame: 1
 
+    ################
+    # Overrides for global object config
+    ################
     objects:
-      person:
-        min_area: 5000
-        max_area: 100000
-        threshold: 0.5
+      track:
+        - person
+      filters:
+        person:
+          min_area: 5000
+          max_area: 100000
+          threshold: 0.5
 
     ################
     # size: size of the region in pixels
     # x_offset/y_offset: position of the upper left corner of your region (top left of image is 0,0)
-    # min_person_area (optional): minimum width*height of the bounding box for the detected person
-    # max_person_area (optional): maximum width*height of the bounding box for the detected person
-    # threshold (optional): The minimum decimal percentage (50% hit = 0.5) for the confidence from tensorflow
     # Tips: All regions are resized to 300x300 before detection because the model is trained on that size.
     #       Resizing regions takes CPU power. Ideally, all regions should be as close to 300x300 as possible.
     #       Defining a region that goes outside the bounds of the image will result in errors.
@@ -111,18 +122,9 @@ cameras:
       - size: 350
         x_offset: 0
         y_offset: 300
-        objects:
-          car:
-            threshold: 0.2
       - size: 400
         x_offset: 350
         y_offset: 250
-        objects:
-          person:
-            min_area: 2000
       - size: 400
         x_offset: 750
         y_offset: 250
-        objects:
-          person:
-            min_area: 2000