README.md

---
license: mit
license_link: LICENSE
library_name: openvino
pipeline_tag: object-detection
tags:
  - openvino
  - intel
  - yolo
  - yolo26
  - perimeter-breach-detection
  - intrusion-detection
  - zone-analytics
  - tracking
  - gstanalytics
  - gvaanalytics
  - edge-ai
  - metro
  - dlstreamer
language:
  - en
---

# Perimeter Breach Detection

| Property | Value |
|---|---|
| **Category** | Object Detection + Tracking + Zone Analytics (GstAnalytics) |
| **Base Model** | [YOLO26](https://docs.ultralytics.com/models/yolo26/) (Ultralytics) |
| **Source Framework** | PyTorch (Ultralytics) |
| **Supported Precisions** | FP32, FP16, INT8 (mixed-precision) |
| **Inference Engine** | OpenVINO |
| **Hardware** | CPU, GPU, NPU |
| **Detected Class** | `person` (COCO class 0) |

---

## Overview

Perimeter Breach Detection is a Metro Analytics use case that flags people who breach a secured perimeter marked by yellow and black safety tape.
It is built on [YOLO26](https://docs.ultralytics.com/models/yolo26/) for person detection, paired with a multi-object tracker that assigns persistent IDs across frames.
The restricted region is a polygon that traces the safety tape: in the bundled sample video the yellow and black tape forms a diagonal boundary across the floor, and the default zone covers the keep-out side of that boundary.
A person whose center falls inside the polygon is reported as a perimeter breach.
The model is a quantized (INT8) state-of-the-art detector; smaller variants run at high FPS on edge hardware.

Typical Metro deployments include:

- **Fence-Line Protection** -- trigger when a person crosses a fence or barrier around depots, yards, or substations.
- **Secured-Transportation Facilities** -- monitor taped-off loading docks, platforms, and maintenance bays that must stay clear.
- **Work-Zone Safety** -- alert when a worker or bystander enters a taped hazard area near equipment or track work.
- **Restricted-Area Enforcement** -- raise alerts when anyone enters a standoff boundary marked with safety tape.

Available variants: `yolo26n`, `yolo26s`, `yolo26m`, `yolo26l`, `yolo26x`.
Smaller variants (`yolo26n`, `yolo26s`) are recommended for high-FPS edge deployment.

---

## Prerequisites

- Python 3.11+
- [Install OpenVINO](https://docs.openvino.ai/2026/get-started/install-openvino.html) (latest version)
- [Install Intel DLStreamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/get_started/install/install_guide_ubuntu.html) (latest version)

Create and activate a Python virtual environment before running the scripts:

```bash
python3 -m venv .venv --system-site-packages
source .venv/bin/activate
```

> **Note:** The `--system-site-packages` flag is required so the virtual
> environment can access the system-installed OpenVINO and DLStreamer Python
> packages.

---

## Getting Started

### Download and Quantize Model

Run the provided script to download, export to OpenVINO IR, and optionally quantize:

```bash
chmod +x export_and_quantize.sh
./export_and_quantize.sh
```

This exports the default **yolo26n** model in **FP16** precision.

#### Optional: Select a Different Variant or Precision

```bash
./export_and_quantize.sh yolo26n FP32   # full-precision
./export_and_quantize.sh yolo26n INT8   # quantized
./export_and_quantize.sh yolo26s        # larger variant, default FP16
```

Replace `yolo26n` with any variant (`yolo26s`, `yolo26m`, `yolo26l`, `yolo26x`).
The second argument selects the precision (`FP32`, `FP16`, `INT8`); the default is **FP16**.

The script performs the following steps:

1. Installs dependencies (`openvino`, `ultralytics`, `opencv-python`; adds `nncf` for INT8).
2. Downloads the sample worker-zone video (`worker-zone-detection.mp4`) into the current directory.
3. Downloads the PyTorch weights and exports to OpenVINO IR.
4. *(INT8 only)* Quantizes the model using NNCF post-training quantization.

Output files:

- `yolo26n_openvino_model/` -- FP32 or FP16 OpenVINO IR model directory.
- `yolo26n_perimeter_int8.xml` / `yolo26n_perimeter_int8.bin` -- INT8 quantized model *(only when `INT8` is selected)*.

#### Precision / Device Compatibility

| Precision | CPU | GPU | NPU |
|---|---|---|---|
| FP32 | Yes | Yes | No |
| FP16 | Yes | Yes | Yes |
| INT8 | Yes | Yes | Yes |

> **Note:** The INT8 calibration uses frames from the bundled sample video.
> For production accuracy, replace it with a representative set of frames from
> the target deployment site.

### OpenVINO Sample

The sample below runs YOLO26 inference on the sample video and flags any person whose center falls inside the restricted zone as a breach.
The zone is the `RESTRICTED_ZONE` polygon, which traces the yellow and black safety tape in the sample video's `1920x1080` pixel space: the tape runs as a diagonal boundary from `(1577, 0)` to `(434, 1079)`, and the polygon covers the keep-out side of that line.
To adapt the perimeter to a different camera, edit the `RESTRICTED_ZONE` points.
YOLO26 is end-to-end (NMS-free), so no manual non-maximum suppression is needed.
Breaching people are drawn with a red box and a `BREACH` label; the annotated result is written to `output_openvino.mp4`.
Change the `device` string to run on CPU, GPU, or NPU.

```python
import cv2
import numpy as np
import openvino as ov

PERSON_CLASS_ID = 0
CONF_THRESHOLD = 0.4
INPUT_SIZE = 640
INPUT_VIDEO = "worker-zone-detection.mp4"

# Restricted zone traced from the yellow-and-black safety tape in the sample
# video (1920x1080). The tape runs diagonally from (1577, 0) to (434, 1079);
# this polygon covers the keep-out side of that boundary. Edit these points to
# retrace the tape for a different camera.
RESTRICTED_ZONE = np.array([[0, 0], [1577, 0], [434, 1079], [0, 1080]], dtype=np.int32)

core = ov.Core()
model = core.read_model("yolo26n_openvino_model/yolo26n.xml")

# Change device to "GPU" or "NPU" to run on integrated GPU or NPU.
compiled = core.compile_model(model, "CPU")

cap = cv2.VideoCapture(INPUT_VIDEO)
fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
w0 = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h0 = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

zone = RESTRICTED_ZONE

writer = cv2.VideoWriter(
    "output_openvino.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w0, h0)
)

sx, sy = w0 / INPUT_SIZE, h0 / INPUT_SIZE
frame_idx = 0
while True:
    ok, frame = cap.read()
    if not ok:
        break
    frame_idx += 1

    blob = cv2.resize(frame, (INPUT_SIZE, INPUT_SIZE))
    blob = cv2.cvtColor(blob, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
    blob = blob.transpose(2, 0, 1)[np.newaxis, ...]  # NCHW

    # YOLO26 end-to-end output: [1, 300, 6] = [x1, y1, x2, y2, confidence, class_id]
    output = compiled([blob])[compiled.output(0)][0]
    mask = (output[:, 4] >= CONF_THRESHOLD) & (output[:, 5].astype(int) == PERSON_CLASS_ID)

    # Draw the translucent restricted zone first, then the person boxes on top.
    overlay = frame.copy()
    cv2.fillPoly(overlay, [zone], (0, 0, 255))
    cv2.addWeighted(overlay, 0.25, frame, 0.75, 0, frame)
    cv2.polylines(frame, [zone], True, (0, 255, 255), 2)

    breaches = 0
    for det in output[mask]:
        x1, y1 = int(det[0] * sx), int(det[1] * sy)
        x2, y2 = int(det[2] * sx), int(det[3] * sy)
        center = (int((x1 + x2) / 2), int((y1 + y2) / 2))
        inside = cv2.pointPolygonTest(zone, center, False) >= 0
        color = (0, 0, 255) if inside else (0, 255, 0)
        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
        if inside:
            breaches += 1
            cv2.putText(
                frame, "BREACH", (x1, max(y1 - 6, 12)),
                cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2,
            )

    cv2.putText(
        frame, f"Breaches: {breaches}", (10, 30),
        cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2,
    )
    if breaches:
        print(f"frame {frame_idx}: perimeter breach - {breaches} person(s) inside zone", flush=True)
    writer.write(frame)

cap.release()
writer.release()
print("Saved: output_openvino.mp4")
```

**Device targets:**

- `"CPU"` -- default, works on all Intel platforms.
- `"GPU"` -- Intel integrated or discrete GPU.
- `"NPU"` -- Intel NPU (different throughput profile; validate with `benchmark_app -d NPU`).

#### Expected Output

![OpenVINO expected output](expected_output_openvino.gif)

`output_openvino.mp4` shows the restricted perimeter shaded in red, a green box around each person outside the zone, and a red `BREACH` box around anyone inside it.

### DLStreamer Sample

The pipeline below runs the FP16 YOLO26 detector on the sample video via `gvadetect`, tracks each person with `gvatrack`, and uses DLStreamer's `gvaanalytics` element to test membership in the restricted zone.
The zone is the same `RESTRICTED_ZONE` polygon used by the OpenVINO sample, passed to `gvaanalytics` as a JSON zone, so no polygon math is required in the code.
`gvaanalytics` attaches `GstAnalyticsZoneMtd` to every tracked person whose center falls inside the polygon.
The pipeline ends in an `appsink`; for each frame a callback reads the analytics metadata, shades the restricted zone, draws a green box around people outside it and a red `BREACH` box around anyone inside, and writes the annotated result to `output_dlstreamer.mp4`.
A perimeter-breach event is printed the first time each tracked person enters the zone.

> **Notes on running this sample:**
>
> - Use the FP16 IR (`yolo26n_openvino_model/yolo26n.xml`).
>   On DLStreamer 2026.1, `gvadetect` cannot auto-derive a YOLO post-processor
>   from the INT8 model produced by the bundled script.
>   To use the INT8 model, supply a matching `model-proc` JSON.
> - Class names are read automatically from the model's embedded
>   `metadata.yaml` by DLStreamer 2026.0+ -- no external `labels-file` is
>   required.
> - Export `PYTHONPATH` so the DLStreamer Python module is importable:
>
>   ```bash
>   source /opt/intel/openvino_2026/setupvars.sh
>   source /opt/intel/dlstreamer/scripts/setup_dls_env.sh
>   export PYTHONPATH=/opt/intel/dlstreamer/python:\
>   /opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:${PYTHONPATH:-}
>   ```

```python
import json
import sys
import gi

gi.require_version("Gst", "1.0")
gi.require_version("GstApp", "1.0")
gi.require_version("GstAnalytics", "1.0")
gi.require_version("DLStreamerMeta", "1.0")
from gi.repository import Gst, GLib, GstApp, GstAnalytics, DLStreamerMeta

Gst.init([])

# Register DLStreamerMeta types so GstAnalytics iteration can handle them.
_ov = sys.modules["gi.overrides.GstAnalytics"]
_ov.__mtd_types__[DLStreamerMeta.ZoneMtd.get_mtd_type()] = DLStreamerMeta.relation_meta_get_zone_mtd

# Import OpenCV after Gst.init to avoid a GStreamer re-initialization conflict.
import cv2
import numpy as np

MODEL = "yolo26n_openvino_model/yolo26n.xml"
VIDEO = "worker-zone-detection.mp4"
DEVICE = "GPU"  # change to "CPU" or "NPU" as needed

# Restricted zone traced from the yellow-and-black safety tape in the sample
# video (1920x1080), matching the OpenVINO sample. Edit these points to retrace
# the tape for a different camera.
RESTRICTED_ZONE = np.array([[0, 0], [1577, 0], [434, 1079], [0, 1080]], dtype=np.int32)
ZONE_JSON = json.dumps([{
    "id": "restricted_zone",
    "type": "polygon",
    "points": [{"x": int(x), "y": int(y)} for x, y in RESTRICTED_ZONE],
}])

pipeline = Gst.parse_launch(
    f"filesrc location={VIDEO} ! decodebin3 ! videoconvert ! "
    f"gvadetect model={MODEL} device={DEVICE} threshold=0.4 ! queue ! "
    f"gvatrack tracking-type=short-term-imageless ! queue ! "
    f"gvaanalytics name=analytics ! queue ! "
    f"videoconvert ! video/x-raw,format=BGR ! "
    f"appsink name=sink emit-signals=true max-buffers=4 drop=false sync=false"
)
pipeline.get_by_name("analytics").set_property("zones", ZONE_JSON)

writer = {"w": None}
# Track IDs that have already triggered a breach event, so each intruder is
# reported only once.
flagged = set()


def on_sample(appsink):
    sample = appsink.emit("pull-sample")
    if sample is None:
        return Gst.FlowReturn.OK
    buf = sample.get_buffer()
    caps = sample.get_caps().get_structure(0)
    w = caps.get_value("width")
    h = caps.get_value("height")
    ok_fr, fr_n, fr_d = caps.get_fraction("framerate")
    fps = (fr_n / fr_d) if (ok_fr and fr_d) else 30.0

    ok, minfo = buf.map(Gst.MapFlags.READ)
    if not ok:
        return Gst.FlowReturn.OK
    frame = np.ndarray((h, w, 3), buffer=minfo.data, dtype=np.uint8).copy()
    buf.unmap(minfo)

    now = buf.pts / Gst.SECOND if buf.pts != Gst.CLOCK_TIME_NONE else 0.0

    # Shade the restricted zone and outline the tape boundary.
    overlay = frame.copy()
    cv2.fillPoly(overlay, [RESTRICTED_ZONE], (0, 0, 255))
    cv2.addWeighted(overlay, 0.25, frame, 0.75, 0, frame)
    cv2.polylines(frame, [RESTRICTED_ZONE], True, (0, 255, 255), 2)

    breaches = 0
    rmeta = GstAnalytics.buffer_get_analytics_relation_meta(buf)
    if rmeta:
        for od in rmeta.iter_on_type(GstAnalytics.ODMtd):
            if GLib.quark_to_string(od.get_obj_type()) != "person":
                continue
            _, x, y, bw, bh, _ = od.get_location()

            # Find the tracking ID via the direct relation.
            track_id = None
            for trk in od.iter_direct_related(GstAnalytics.RelTypes.RELATE_TO, GstAnalytics.TrackingMtd):
                success, tid, *_ = trk.get_info()
                if success:
                    track_id = tid
                break

            # gvaanalytics attaches a ZoneMtd relation when the person is in the zone.
            in_zone = any(
                True for _ in od.iter_direct_related(GstAnalytics.RelTypes.RELATE_TO, DLStreamerMeta.ZoneMtd)
            )
            color = (0, 0, 255) if in_zone else (0, 255, 0)
            cv2.rectangle(frame, (int(x), int(y)), (int(x + bw), int(y + bh)), color, 3)
            label = f"id {track_id}" if track_id is not None else "person"
            if in_zone:
                breaches += 1
                cv2.putText(frame, f"BREACH {label}", (int(x), max(int(y) - 8, 14)),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
                if track_id is not None and track_id not in flagged:
                    flagged.add(track_id)
                    print(f"PERIMETER BREACH id={track_id} t={now:.1f}s "
                          f"entered restricted zone at ({int(x + bw / 2)},{int(y + bh)})", flush=True)
            else:
                cv2.putText(frame, label, (int(x), max(int(y) - 8, 14)),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)

    cv2.putText(frame, f"Breaches: {breaches}", (10, 40),
                cv2.FONT_HERSHEY_SIMPLEX, 1.1, (0, 0, 255), 3)
    if writer["w"] is None:
        writer["w"] = cv2.VideoWriter(
            "output_dlstreamer.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)
        )
    writer["w"].write(frame)
    return Gst.FlowReturn.OK


pipeline.get_by_name("sink").connect("new-sample", on_sample)
pipeline.set_state(Gst.State.PLAYING)
pipeline.get_bus().timed_pop_filtered(Gst.CLOCK_TIME_NONE, Gst.MessageType.EOS | Gst.MessageType.ERROR)
pipeline.set_state(Gst.State.NULL)
if writer["w"] is not None:
    writer["w"].release()
```

Expected output:

```text
PERIMETER BREACH id=2 t=3.8s entered restricted zone at (799,1067)
PERIMETER BREACH id=8 t=13.7s entered restricted zone at (789,1069)
...
```

The annotated video is saved to `output_dlstreamer.mp4`.
It shows the restricted zone shaded in red with the tape boundary outlined, a green box around each person outside the zone, and a red `BREACH` box around anyone inside it -- matching the OpenVINO output.

#### Expected Output

![DLStreamer expected output](expected_output_dlstreamer.gif)

**Device targets:**

- `DEVICE = "GPU"` -- default in the sample code.
- `DEVICE = "CPU"` -- change `DEVICE = "GPU"` to `DEVICE = "CPU"`.
- `DEVICE = "NPU"` -- change `DEVICE = "GPU"` to `DEVICE = "NPU"` for the Intel NPU.

---

## License

Licensed under the MIT License. See [LICENSE](LICENSE) for details.

## References

- [YOLO26 Documentation](https://docs.ultralytics.com/models/yolo26/)
- [OpenVINO YOLO26 Notebook](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/yolov26-optimization/yolov26-object-detection.ipynb)
- [OpenVINO Documentation](https://docs.openvino.ai/)
- [NNCF Post-Training Quantization](https://docs.openvino.ai/latest/nncf_ptq_introduction.html)
- [Intel DLStreamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/index.html)