diff --git a/docs/how_to/count_in_zone.md b/docs/how_to/count_in_zone.md
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+With supervision, you can count the number of objects in a zone in an image or video. In this guide, we will show how to count the number of cars in a traffic video.
+
+[View the notebook that accompanies this tutorial](https://github.com/roboflow/notebooks/blob/main/notebooks/how-to-use-polygonzone-annotate-and-supervision.ipynb).
+
+To make it easier for you to follow our tutorial download the video we will use as an example. You can do this using the `supervision.assets` module:
+
+```python
+from supervision.assets import download_assets, VideoAssets
+
+download_assets(VideoAssets.VEHICLES_2)
+```
+
+## Initialize a Model and Load Video
+
+First, we need to initialize a model. Let's use a YOLOv8 model with the default COCO checkpoint. We also need to load a video on which to run inference.
+
+```python
+import numpy as np
+import supervision as sv
+import cv2
+
+from ultralytics import YOLO
+
+model = YOLO('yolov8s.pt')
+
+VIDEO = str(VideoAssets.VEHICLES_2)
+
+colors = sv.ColorPalette.default()
+video_info = sv.VideoInfo.from_video_path(VIDEO)
+```
+
+## Calculate Coordinates
+
+To count objects in a zone, you need to know the coordinates where you want to draw the zone.
+
+You can calculate coordinates using the [PolygonZone web utility](https://roboflow.github.io/polygonzone/).
+
+To use the PolygonZone website, you will need to upload an image or frame from a video. You can retrieve a frame using this code:
+
+```python
+generator = sv.get_video_frames_generator(VIDEO)
+iterator = iter(generator)
+
+frame = next(iterator)
+
+cv2.imwrite("first_frame.png", frame)
+```
+
+PolygonZone will give you NumPy arrays that you can use with supervision to count objects in zones.
+
+<video width="100%" loop muted autoplay>
+  <source src="https://media.roboflow.com/polygonzone.mp4" type="video/mp4">
+</video>
+
+Save the coordinates in an array:
+
+```python
+polygons = [
+  np.array([
+    [718, 595],[927, 592],[851, 1062],[42, 1059]
+  ]),
+  np.array([
+    [987, 595],[1199, 595],[1893, 1056],[1015, 1062]
+  ])
+]
+```
+
+## Define Zones
+
+With the coordinates of the zones to draw ready, we can set up our zones:
+
+```python
+zones = [
+    sv.PolygonZone(
+        polygon=polygon,
+        frame_resolution_wh=video_info.resolution_wh
+    )
+    for polygon
+    in polygons
+]
+zone_annotators = [
+    sv.PolygonZoneAnnotator(
+        zone=zone,
+        color=colors.by_idx(index),
+        thickness=4,
+        text_thickness=8,
+        text_scale=4
+    )
+    for index, zone
+    in enumerate(zones)
+]
+box_annotators = [
+    sv.BoxAnnotator(
+        color=colors.by_idx(index),
+        thickness=4,
+        text_thickness=4,
+        text_scale=2,
+        )
+    for index
+    in range(len(polygons))
+]
+```
+
+## Run Inference
+
+We can run inference on a video using the [sv.process_video](https://supervision.roboflow.com/utils/video/#process_video) function. This function accepts a callback that runs inference on each frame and compiles the results into a video.
+
+Below, we can call our YOLOv8 model, annotate predictions and zones, then save the results to a file called `result.mp4`.
+
+```python
+def process_frame(frame: np.ndarray, i) -> np.ndarray:
+    results = model(frame, imgsz=1280, verbose=False)[0]
+    detections = sv.Detections.from_ultralytics(results)
+
+    for zone, zone_annotator, box_annotator in zip(zones, zone_annotators, box_annotators):
+        mask = zone.trigger(detections=detections)
+        detections_filtered = detections[mask]
+        frame = box_annotator.annotate(scene=frame, detections=detections_filtered, skip_label=True)
+        frame = zone_annotator.annotate(scene=frame)
+
+    return frame
+
+sv.process_video(source_path=VIDEO, target_path="result.mp4", callback=process_frame)
+```
+
+Here is an example of inference run on the video:
+
+<video width="100%" loop muted autoplay>
+  <source src="https://blog.roboflow.com/content/media/2023/03/trim-counting.mp4" type="video/mp4">
+</video>
diff --git a/mkdocs.yml b/mkdocs.yml
index a6be0a7b..61852237 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -38,6 +38,7 @@ nav:
       - Track Objects on Video: how_to/track_objects.md
       - Process Datasets: how_to/process_datasets.md
       - Benchmark a Model: how_to/benchmark_a_model.md
+      - Count in Zone: how_to/count_in_zone.md
   - Reference:
       - Detection and Segmentation:
           - Core: detection/core.md