---
comments: true
description: Count objects entering a polygon zone in images and video using supervision's PolygonZone — measure throughput and density in any region.
authors:
  - name: Piotr Skalski
    role: Computer Vision Engineer, Roboflow
    github: https://github.com/SkalskiP
date_modified: 2026-04-22
---

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.

Create a YOLO model instance and download the source video. The model will process each frame during inference. A shared color palette ensures consistent zone coloring throughout the output video.

```python
import numpy as np
import supervision as sv
import cv2

from ultralytics import YOLO
from supervision.assets import VideoAssets, download_assets

model = YOLO("yolov8s.pt")

VIDEO = download_assets(VideoAssets.VEHICLES_2)

colors = sv.ColorPalette.DEFAULT
```

## 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:

Instantiate a `PolygonZone` for each polygon array, pairing it with a `PolygonZoneAnnotator` for visual overlay and a `BoxAnnotator` for drawing detection boxes. Each zone will later trigger on incoming detections to determine which objects fall inside its boundaries, enabling per-zone counting in the inference callback.

```python
zones = [sv.PolygonZone(polygon=polygon) 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,
    )
    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)
        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>

## Frequently Asked Questions

### How do I count objects in a zone with supervision?

Create `sv.PolygonZone` with a polygon defining your region. Call `zone.trigger(detections)` on each frame — it returns a mask of detections inside the zone.

### Can I count objects crossing a line instead of entering a zone?

Yes. Use `sv.LineZone` — define a start and end point. `zone.trigger(detections)` returns a tuple of two boolean arrays, `(crossed_in, crossed_out)`, indicating which detections crossed the line in each direction. `LineZone` requires `detections.tracker_id`; run a tracker first so the same object can be matched across frames.

### Can I combine zone counting with tracking?

Yes. You can pass tracker IDs from `sv.ByteTrack` alongside your detections, but `sv.PolygonZone` still evaluates the zone on each frame and reports which objects are currently inside it. If you want to count each object only once when it first enters the zone, maintain a set of seen `tracker_id` values after filtering detections with `zone.trigger(detections)`, or use a dedicated entry/crossing counting tool such as `sv.LineZone` when it better matches your use case.

## Author

- [Piotr Skalski](https://github.com/SkalskiP) — Computer Vision Engineer, Roboflow