Metro Analytics Catalog
Collection
AI models and analytics pipelines for Intel hardware to help developers build and deploy edge AI solutions quickly. • 27 items • Updated • 1
Perimeter Breach Detection
| Property | Value |
|---|---|
| Category | Object Detection + Tracking + Zone Analytics (GstAnalytics) |
| Base Model | 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 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 (latest version)
- Install Intel DLStreamer (latest version)
Create and activate a Python virtual environment before running the scripts:
python3 -m venv .venv --system-site-packages
source .venv/bin/activate
Note: The
--system-site-packagesflag 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:
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
./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:
- Installs dependencies (
openvino,ultralytics,opencv-python; addsnncffor INT8). - Downloads the sample worker-zone video (
worker-zone-detection.mp4) into the current directory. - Downloads the PyTorch weights and exports to OpenVINO IR.
- (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 whenINT8is 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.
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 withbenchmark_app -d NPU).
Expected Output
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,gvadetectcannot auto-derive a YOLO post-processor from the INT8 model produced by the bundled script. To use the INT8 model, supply a matchingmodel-procJSON.Class names are read automatically from the model's embedded
metadata.yamlby DLStreamer 2026.0+ -- no externallabels-fileis required.Export
PYTHONPATHso the DLStreamer Python module is importable: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:-}
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:
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
Device targets:
DEVICE = "GPU"-- default in the sample code.DEVICE = "CPU"-- changeDEVICE = "GPU"toDEVICE = "CPU".DEVICE = "NPU"-- changeDEVICE = "GPU"toDEVICE = "NPU"for the Intel NPU.
License
Licensed under the MIT License. See LICENSE for details.