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person-detection

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Person Detection

Property	Value
Category	Object Detection (Person Detection)
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

Person Detection is a Metro Analytics use case that detects and localizes people in images and video streams. It is built on YOLO26, a state-of-the-art real-time object detector trained on the COCO dataset, quantized to INT8 and filtered at runtime to the person class. The quantized INT8 model delivers high throughput on Intel CPUs, GPUs, and NPUs while maintaining strong detection accuracy.

Typical Metro deployments include:

Occupancy Counting -- count people on platforms, in lobbies, or at transit stops.
Pedestrian Flow Analysis -- measure directional foot traffic at gates and corridors.
Access Control -- detect unauthorized presence in restricted zones.
Public Safety -- support situational awareness in transit hubs and venues.

Available variants: yolo26n, yolo26s, yolo26m, yolo26l, yolo26x. Smaller variants (yolo26n, yolo26s) are recommended for high-FPS edge deployment; larger variants improve recall in dense scenes.

Prerequisites

Python 3.11+
Install OpenVINO (latest version)
Install Intel DLStreamer (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:

Installs dependencies (openvino, ultralytics; adds nncf for INT8).
Downloads a sample test image (test.jpg) and a sample test video (test_video.mp4).
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_person_int8.xml / yolo26n_person_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 the bundled sample image. For production accuracy, replace it with a representative set of frames from the target deployment site.

OpenVINO Sample

The sample below runs YOLO26 inference, filters to the person class, and reports the person count for a single image. YOLO26 is end-to-end (NMS-free), so no manual non-maximum suppression is needed. 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

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")

image = cv2.imread("test.jpg")
h0, w0 = image.shape[:2]

blob = cv2.resize(image, (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)
dets = output[mask]

sx, sy = w0 / INPUT_SIZE, h0 / INPUT_SIZE
person_count = len(dets)
print(f"Detected persons: {person_count}")

for det in dets:
    x1 = int(det[0] * sx)
    y1 = int(det[1] * sy)
    x2 = int(det[2] * sx)
    y2 = int(det[3] * sy)
    cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)

cv2.putText(
    image, f"Persons: {person_count}", (10, 30),
    cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2,
)
cv2.imwrite("output_openvino.jpg", image)

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).

Try It on a Sample Image

The export_and_quantize.sh script downloads test.jpg automatically. Re-run the OpenVINO sample above. The script reads test.jpg, prints the person count to the console, and writes the annotated frame to output_openvino.jpg.

Expected console output:

Detected persons: 2

output_openvino.jpg shows a green bounding box around each detected person and the text Persons: 2 in the top-left corner.

Expected Output

DLStreamer Sample

The pipeline below runs the FP16 YOLO26 detector on the sample video via gvadetect, filters detections to the person class in a buffer probe using the GStreamer Analytics metadata API (GstAnalytics), overlays bounding boxes, saves the annotated result to output_dlstreamer.mp4, and prints the person count per frame.

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
1source /opt/intel/openvino_2026/setupvars.sh 2source /opt/intel/dlstreamer/scripts/setup_dls_env.sh 3export PYTHONPATH=/opt/intel/dlstreamer/python:\ 4/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:${PYTHONPATH:-}

python

import gi

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

Gst.init([])

INPUT_VIDEO = "test_video.mp4"

# For CPU: change device=GPU to device=CPU.
# For NPU: change device=GPU to device=NPU (batch-size=1, nireq=4 recommended).
pipeline_str = (
    f"filesrc location={INPUT_VIDEO} ! decodebin3 ! "
    "videoconvert ! "
    "gvadetect model=yolo26n_openvino_model/yolo26n.xml "
    "device=GPU "
    "threshold=0.4 ! queue ! "
    "gvawatermark displ-cfg=show-roi=person ! "
    "videoconvert ! video/x-raw,format=I420 ! "
    "openh264enc ! h264parse ! "
    "mp4mux ! filesink name=sink location=output_dlstreamer.mp4"
)
pipeline = Gst.parse_launch(pipeline_str)


def on_buffer(pad, info):
    buf = info.get_buffer()
    rmeta = GstAnalytics.buffer_get_analytics_relation_meta(buf)
    if rmeta is None:
        return Gst.PadProbeReturn.OK
    person_count = 0
    idx = 1
    while True:
        ok, od = rmeta.get_od_mtd(idx)
        if not ok:
            break
        if GLib.quark_to_string(od.get_obj_type()) == "person":
            person_count += 1
        idx += 1
    if person_count:
        print(f"Person count: {person_count}", flush=True)
    return Gst.PadProbeReturn.OK


sink = pipeline.get_by_name("sink")
sink_pad = sink.get_static_pad("sink")
sink_pad.add_probe(Gst.PadProbeType.BUFFER, on_buffer)

pipeline.set_state(Gst.State.PLAYING)
bus = pipeline.get_bus()
bus.timed_pop_filtered(
    Gst.CLOCK_TIME_NONE,
    Gst.MessageType.EOS | Gst.MessageType.ERROR,
)
pipeline.set_state(Gst.State.NULL)

Expected Output

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; use batch-size=1 and nireq=4 for best NPU utilization.

License

Licensed under the MIT License. See LICENSE for details.

Intel/person-detection

Person Detection

Overview

Prerequisites

Getting Started

Download and Quantize Model

Optional: Select a Different Variant or Precision

Precision / Device Compatibility

OpenVINO Sample

Try It on a Sample Image

Expected Output

DLStreamer Sample

Expected Output

License

References

Intel/
person-detection