--- 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)