README.md

---
license: mit
license_link: LICENSE
library_name: openvino
pipeline_tag: object-detection
tags:
  - openvino
  - intel
  - yolo
  - yolo26
  - running-detection
  - speed-estimation
  - tracking
  - edge-ai
  - metro
  - dlstreamer
language:
  - en
---

# Running Detection

| Property | Value |
|---|---|
| **Category** | Object Detection + Tracking + Speed Estimation |
| **Base Model** | [YOLO26](https://docs.ultralytics.com/models/yolo26/) (Ultralytics) + DLStreamer `gvatrack` (Kalman filter tracker) |
| **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

Running Detection is a Metro Analytics use case that flags people who are running or moving faster than a configurable speed threshold.
It is built on [YOLO26](https://docs.ultralytics.com/models/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.
Each detected person is assigned a persistent track ID across frames, and per-track speed is estimated from the frame-to-frame displacement of the bounding-box center.
A person is flagged as running when the estimated speed stays above the threshold for a short, sustained window, which suppresses single-frame jitter.

Typical Metro deployments include:

- **Platform Safety** -- flag people sprinting across platforms or toward closing train doors.
- **Incident Detection** -- surface sudden running that may indicate a chase, altercation, or emergency.
- **Crowd Flow Monitoring** -- distinguish normal walking pace from abnormal fast movement in concourses.
- **Restricted-Speed Zones** -- enforce walk-only areas such as escalators, ramps, and stairwells.

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+
- `ffmpeg` (`sudo apt install ffmpeg`) -- used by the samples to encode output video
- [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 running video (`running.mp4`) and extracts a calibration frame (`test.jpg`).
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_running_int8.xml` / `yolo26n_running_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 extracted sample frame.
> For production accuracy, replace it with a representative set of frames from
> the target deployment site.

### Speed Threshold

Running is defined by a per-track speed threshold expressed in pixels per second:

```text
RUNNING_SPEED = 250.0   # pixels/second (demo value for the sample clip)
MIN_RUN_FRAMES = 3      # sustained frames above the threshold before flagging
```

> **Note:** Pixel speed depends on camera resolution, framing, and distance to
> the subject, so `RUNNING_SPEED` must be tuned per site.  For a calibrated
> metric speed (meters/second), convert pixel displacement using the known
> ground-sampling distance of the scene.

### OpenVINO Sample

The sample below runs YOLO26 inference on the sample video, filters to the `person` class, assigns track IDs with a lightweight nearest-center tracker, estimates per-track pixel speed, and flags people who run faster than `RUNNING_SPEED` for at least `MIN_RUN_FRAMES` frames.
YOLO26 is end-to-end (NMS-free), so no manual non-maximum suppression is needed.
The annotated result is written to `output_openvino.mp4`, with a latched
`RUNNING DETECTED` / `NO RUNNING DETECTED` status banner across the top.
Change the `DEVICE` string to run on CPU, GPU, or NPU.

```python
import subprocess

import cv2
import numpy as np
import openvino as ov

PERSON_CLASS_ID = 0
CONF_THRESHOLD = 0.4
INPUT_SIZE = 640
RUNNING_SPEED = 250.0   # pixels/second
MIN_RUN_FRAMES = 3      # sustained frames above the threshold before flagging
MAX_MATCH_DIST = 120    # max center distance (px) to link a track across frames
ALERT_HOLD_SECONDS = 2.0  # latch the alert banner to keep it from flickering

# Change DEVICE to "GPU" or "NPU" to run on integrated GPU or NPU.
DEVICE = "CPU"
INPUT_VIDEO = "running.mp4"

core = ov.Core()
model = core.read_model("yolo26n_openvino_model/yolo26n.xml")
compiled = core.compile_model(model, DEVICE)
output_port = compiled.output(0)

cap = cv2.VideoCapture(INPUT_VIDEO)
fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
ALERT_HOLD_FRAMES = max(1, int(ALERT_HOLD_SECONDS * fps))

proc = subprocess.Popen(
    ["ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "bgr24",
     "-s", f"{width}x{height}", "-r", str(fps),
     "-i", "pipe:0", "-c:v", "libx264", "-pix_fmt", "yuv420p",
     "-movflags", "+faststart", "output_openvino.mp4"],
    stdin=subprocess.PIPE, stderr=subprocess.DEVNULL,
)

tracks: dict[int, dict] = {}   # id -> {cx, cy, run_frames}
next_id = 0
flagged: set[int] = set()
alert_hold = 0
frame_idx = 0

while True:
    ok, frame = cap.read()
    if not ok:
        break
    frame_idx += 1
    dt = 1.0 / fps

    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

    output = compiled([blob])[output_port][0]
    mask = (output[:, 4] >= CONF_THRESHOLD) & (output[:, 5].astype(int) == PERSON_CLASS_ID)
    dets = output[mask]

    sx, sy = width / INPUT_SIZE, height / INPUT_SIZE
    detections = []
    for det in dets:
        x1, y1 = int(det[0] * sx), int(det[1] * sy)
        x2, y2 = int(det[2] * sx), int(det[3] * sy)
        detections.append((x1, y1, x2, y2, (x1 + x2) // 2, (y1 + y2) // 2))

    # Greedy nearest-center association to the previous frame's tracks.
    used = set()
    assignments = {}
    for i, (_, _, _, _, cx, cy) in enumerate(detections):
        best_id, best_dist = None, MAX_MATCH_DIST
        for tid, tr in tracks.items():
            if tid in used:
                continue
            d = np.hypot(cx - tr["cx"], cy - tr["cy"])
            if d < best_dist:
                best_id, best_dist = tid, d
        if best_id is None:
            best_id = next_id
            next_id += 1
            tracks[best_id] = {"cx": cx, "cy": cy, "run_frames": 0}
        used.add(best_id)
        assignments[i] = best_id

    new_tracks = {}
    frame_running = False
    for i, (x1, y1, x2, y2, cx, cy) in enumerate(detections):
        tid = assignments[i]
        prev = tracks.get(tid, {"cx": cx, "cy": cy, "run_frames": 0})
        speed = np.hypot(cx - prev["cx"], cy - prev["cy"]) / dt
        run_frames = prev["run_frames"] + 1 if speed >= RUNNING_SPEED else 0
        new_tracks[tid] = {"cx": cx, "cy": cy, "run_frames": run_frames}

        is_running = run_frames >= MIN_RUN_FRAMES
        frame_running = frame_running or is_running
        color = (0, 0, 255) if is_running else (0, 255, 0)
        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
        label = f"RUNNING {int(speed)}px/s" if is_running else f"{int(speed)}px/s"
        cv2.putText(frame, label, (x1, max(y1 - 8, 12)),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
        if is_running and tid not in flagged:
            flagged.add(tid)
            print(f"RUNNING id={tid} speed={int(speed)}px/s frame={frame_idx}", flush=True)

    tracks = new_tracks

    # Latch the alert so the banner reflects a sustained state, not a single
    # transient frame: once running is seen it stays on for ALERT_HOLD_FRAMES.
    alert_hold = ALERT_HOLD_FRAMES if frame_running else max(0, alert_hold - 1)
    alert_on = alert_hold > 0
    banner = "RUNNING DETECTED" if alert_on else "NO RUNNING DETECTED"
    banner_color = (0, 0, 255) if alert_on else (0, 180, 0)
    cv2.rectangle(frame, (0, 0), (width, 40), (0, 0, 0), -1)
    cv2.putText(frame, banner, (10, 28),
                cv2.FONT_HERSHEY_SIMPLEX, 0.9, banner_color, 2)

    proc.stdin.write(frame.tobytes())

cap.release()
proc.stdin.close()
proc.wait()
print("Wrote output_openvino.mp4", flush=True)
```

**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 console output:

```text
RUNNING id=0 speed=312px/s frame=14
...
Wrote output_openvino.mp4
```

`output_openvino.mp4` shows a green box around each person, turning red with a
`RUNNING` label when the person's speed exceeds the threshold.

#### Expected Output

![OpenVINO expected output](expected_output_openvino.gif)

### DLStreamer Sample

The pipeline below runs the FP16 YOLO26 detector via `gvadetect` on the sample
video, attaches persistent track IDs with `gvatrack`
(`short-term-imageless` tracker), and overlays bounding boxes with
`gvawatermark`.  Frames are pulled from an `appsink`; per-track pixel speed is
computed from the frame-to-frame displacement of each track center, and a
`RUNNING` event is raised when the speed stays above `RUNNING_SPEED` for at
least `MIN_RUN_FRAMES` frames.  A latched `RUNNING DETECTED` /
`NO RUNNING DETECTED` status banner is drawn across the top of every frame.
`gvawatermark` renders boxes for the `person` class only.  The annotated result
is muxed to `output_dlstreamer.mp4`.

> **Notes on running this sample:**
>
> - Use the FP16 IR (`yolo26n_openvino_model/yolo26n.xml`). 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 subprocess
from collections import defaultdict

import numpy as np
import gi

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

Gst.init([])

# Import cv2 after Gst.init to avoid GStreamer re-initialization conflicts.
import cv2

INPUT_VIDEO = "running.mp4"
RUNNING_SPEED = 250.0   # pixels/second
MIN_RUN_FRAMES = 3      # sustained frames above the threshold before flagging
ALERT_HOLD_SECONDS = 2.0  # latch the alert banner to keep it from flickering

# For CPU: change device=GPU to device=CPU.
# For NPU: change device=GPU to device=NPU (batch-size=1, nireq=4 recommended).
# gvawatermark draws only person ROIs (displ-cfg=show-roi=person) so boxes for
# other COCO classes are not rendered.
pipeline_str = (
    f"filesrc location={INPUT_VIDEO} ! decodebin3 ! "
    "videoconvert ! "
    "gvadetect model=yolo26n_openvino_model/yolo26n.xml "
    "device=GPU "
    "threshold=0.4 ! queue ! "
    "gvatrack tracking-type=short-term-imageless ! queue ! "
    "gvawatermark displ-cfg=show-roi=person ! appsink name=sink emit-signals=false sync=false"
)
pipeline = Gst.parse_launch(pipeline_str)
appsink = pipeline.get_by_name("sink")

pipeline.set_state(Gst.State.PLAYING)

proc = None
prev_center: dict[int, tuple[int, int]] = {}
run_frames: dict[int, int] = defaultdict(int)
flagged: set[int] = set()
prev_pts = None
alert_hold = 0
alert_hold_frames = 40   # updated from the real framerate on the first frame
frame_idx = 0

while True:
    sample = appsink.emit("pull-sample")
    if sample is None:
        break

    buf = sample.get_buffer()
    caps = sample.get_caps()
    struct = caps.get_structure(0)
    width = struct.get_value("width")
    height = struct.get_value("height")
    frame_idx += 1

    # Start ffmpeg encoder on the first frame.
    if proc is None:
        ok, fps_num, fps_den = struct.get_fraction("framerate")
        fps = fps_num / fps_den if ok and fps_den > 0 else 25.0
        alert_hold_frames = max(1, int(ALERT_HOLD_SECONDS * fps))
        proc = subprocess.Popen(
            ["ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "bgr24",
             "-s", f"{width}x{height}", "-r", str(fps),
             "-i", "pipe:0", "-c:v", "libx264", "-pix_fmt", "yuv420p",
             "-movflags", "+faststart", "output_dlstreamer.mp4"],
            stdin=subprocess.PIPE, stderr=subprocess.DEVNULL,
        )

    # Elapsed time since the previous frame from buffer timestamps.
    pts = buf.pts / Gst.SECOND if buf.pts != Gst.CLOCK_TIME_NONE else frame_idx / fps
    dt = (pts - prev_pts) if (prev_pts is not None and pts > prev_pts) else 1.0 / fps
    prev_pts = pts

    # Read detection / tracking metadata via GstAnalytics.
    rmeta = GstAnalytics.buffer_get_analytics_relation_meta(buf)
    regions = []
    if rmeta is not None:
        od_entries = []
        trk_map = {}  # metadata_id -> tracking_id
        idx = 1
        while True:
            ok_od, od = rmeta.get_od_mtd(idx)
            ok_trk, trk = rmeta.get_tracking_mtd(idx)
            if not ok_od and not ok_trk:
                break
            if ok_od:
                label = GLib.quark_to_string(od.get_obj_type())
                _, x, y, w, h, _ = od.get_location()
                od_entries.append((idx, label, int(x + w / 2), int(y + h / 2)))
            if ok_trk:
                ok2, tid, _, _, _ = trk.get_info()
                if ok2:
                    trk_map[idx] = tid
            idx += 1
        for od_id, label, cx, cy in od_entries:
            if label != "person":
                continue
            tid = 0
            for trk_meta_id, tracking_id in trk_map.items():
                if rmeta.get_relation(od_id, trk_meta_id) != GstAnalytics.RelTypes.NONE:
                    tid = tracking_id
                    break
            regions.append((tid, cx, cy))

    # Map buffer read-only and copy pixels to a writable numpy array.
    success, map_info = buf.map(Gst.MapFlags.READ)
    if not success:
        continue
    arr = np.ndarray((height, width, 3), dtype=np.uint8,
                     buffer=map_info.data).copy()
    buf.unmap(map_info)

    frame_running = False
    for tid, cx, cy in regions:
        px, py = prev_center.get(tid, (cx, cy))
        speed = np.hypot(cx - px, cy - py) / dt if dt > 0 else 0.0
        prev_center[tid] = (cx, cy)
        run_frames[tid] = run_frames[tid] + 1 if speed >= RUNNING_SPEED else 0

        if run_frames[tid] >= MIN_RUN_FRAMES:
            frame_running = True
            cv2.putText(arr, f"RUNNING {int(speed)}px/s", (cx - 40, max(cy - 20, 12)),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
            if tid not in flagged:
                flagged.add(tid)
                print(f"RUNNING id={tid} speed={int(speed)}px/s frame={frame_idx}", flush=True)

    # Latch the alert so the banner reflects a sustained state, not a single
    # transient frame: once running is seen it stays on for alert_hold_frames.
    alert_hold = alert_hold_frames if frame_running else max(0, alert_hold - 1)
    alert_on = alert_hold > 0
    banner = "RUNNING DETECTED" if alert_on else "NO RUNNING DETECTED"
    banner_color = (0, 0, 255) if alert_on else (0, 180, 0)
    cv2.rectangle(arr, (0, 0), (width, 40), (0, 0, 0), -1)
    cv2.putText(arr, banner, (10, 28),
                cv2.FONT_HERSHEY_SIMPLEX, 0.9, banner_color, 2)

    proc.stdin.write(arr.tobytes())

pipeline.set_state(Gst.State.NULL)
if proc:
    proc.stdin.close()
    proc.wait()
print("Wrote output_dlstreamer.mp4", flush=True)
```

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

---

## License

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

## References

- [YOLO26 Documentation](https://docs.ultralytics.com/models/yolo26/)
- [Ultralytics Multi-Object Tracking](https://docs.ultralytics.com/modes/track/)
- [Intel DLStreamer gvatrack](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/elements/gvatrack.html)
- [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)