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Intel/
motion-tracking

multi-object-tracking

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Motion Tracking

Property	Value
Category	Object Detection + Multi-Object Tracking
Base Model	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(es)	Configurable (default: all 80 COCO classes)

Overview

Motion Tracking is a Metro Analytics use case that detects objects and assigns persistent track IDs across frames, enabling trajectory analysis and temporal event detection. It is built on YOLO26, a state-of-the-art real-time object detector quantized to INT8, paired with a multi-object tracker:

DLStreamer pipeline: YOLO26 FP16 detection via gvadetect + gvatrack element with tracking-type=short-term-imageless.

Each detected object receives a unique track_id that persists across frames as long as the object remains visible. Outputs include per-object trajectories suitable for path analysis, dwell-time computation, and zone-based event triggers.

Typical Metro deployments include:

Pedestrian Trajectory Analysis -- map walking paths through stations for flow optimization.
Dwell-Time Measurement -- measure how long individuals stay in specific zones.
Zone-Based Event Detection -- trigger alerts when tracked objects enter or exit defined areas.
Traffic Flow Analytics -- track vehicles through intersections for signal timing optimization.
Incident Replay -- reconstruct object paths for post-event forensic review.

Available YOLO26 variants: yolo26n, yolo26s, yolo26m, yolo26l, yolo26x. Smaller variants (yolo26n, yolo26s) are recommended for high-FPS edge deployment. The default tracker is short-term-imageless (Kalman filter-based, no image data required). DLStreamer also supports tracking-type=deep-sort for more robust re-identification using a feature extraction model (e.g., mars-small128).

Prerequisites

Python 3.11+
ffmpeg (sudo apt install ffmpeg) -- used by both samples to encode output video
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 the sample test video (test_video.mp4) and a sample test image (test.jpg).
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_tracking_int8.xml / yolo26n_tracking_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.

DLStreamer Sample

The pipeline below runs the YOLO26 FP16 detector via gvadetect on test_video.mp4, attaches persistent track IDs with gvatrack (short-term-imageless tracker), and overlays bounding boxes with gvawatermark. Frames are pulled from an appsink, per-track trajectory polylines are drawn with OpenCV, and the result is muxed to output_dlstreamer.mp4 (H.264 via ffmpeg).

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
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 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

# For CPU: change device=GPU to device=CPU.
# For NPU: change device=GPU to device=NPU (batch-size=1, nireq=4 recommended).
pipeline_str = (
    "filesrc location=test_video.mp4 ! decodebin3 ! "
    "videoconvert ! "
    "gvadetect model=yolo26n_openvino_model/yolo26n.xml "
    "device=GPU "
    "threshold=0.4 ! queue ! "
    "gvatrack tracking-type=short-term-imageless ! queue ! "
    "gvawatermark ! appsink name=sink emit-signals=false sync=false"
)
pipeline = Gst.parse_launch(pipeline_str)
appsink = pipeline.get_by_name("sink")

# Distinct colors for trajectory lines (one per track ID).
COLORS = [
    (255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),
    (255, 0, 255), (0, 255, 255), (128, 0, 255), (255, 128, 0),
]
track_history: dict[int, list[tuple[int, int]]] = defaultdict(list)

pipeline.set_state(Gst.State.PLAYING)

proc = None

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")

    # 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 30.0
        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,
        )

    # Read detection / tracking metadata via GstAnalytics.
    rmeta = GstAnalytics.buffer_get_analytics_relation_meta(buf)
    regions_data = []
    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, conf = 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:
            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_data.append((tid, label, 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)

    # Draw per-track trajectory polylines on the frame copy.
    for tid, label, cx, cy in regions_data:
        track = track_history[tid]
        track.append((cx, cy))
        if len(track) > 30:
            track.pop(0)
        color = COLORS[tid % len(COLORS)]
        pts = np.array(track, dtype=np.int32).reshape((-1, 1, 2))
        cv2.polylines(arr, [pts], False, color, 2)
        print(f"  Track {tid}: {label} center=({cx},{cy})", flush=True)

    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

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/motion-tracking

Motion Tracking

Overview

Prerequisites

Getting Started

Download and Quantize Model

Optional: Select a Different Variant or Precision

Precision / Device Compatibility

DLStreamer Sample

Expected Output

License

References

Intel/
motion-tracking