removed-openvino-op

LICENSE

@@ -1,21 +1,57 @@
-MIT License
-
-Copyright (c) Intel Corporation.
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE
+This directory contains two categories of content under different licenses.
+
+
+Scripts and Documentation
+-------------------------
+
+The scripts (export_and_quantize.sh) and documentation (README.md) in this
+directory are original works by Intel Corporation, licensed under the
+MIT License.
+
+    Copyright (C) Intel Corporation
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+
+YOLO26 Model
+------------
+
+The YOLO26 model weights and the Ultralytics framework are developed by
+Ultralytics and licensed under the GNU Affero General Public License v3.0
+(AGPL-3.0).
+
+    Source:  https://github.com/ultralytics/ultralytics
+    License: https://github.com/ultralytics/ultralytics/blob/main/LICENSE
+    Docs:    https://docs.ultralytics.com/models/yolo26/
+
+Users must comply with the AGPL-3.0 license terms when using, modifying,
+or distributing the YOLO26 model weights or Ultralytics software.
+For commercial licensing options, see https://www.ultralytics.com/license.
+
+
+BoT-SORT / ByteTrack Trackers
+------------------------------
+
+The BoT-SORT and ByteTrack tracking algorithms are integrated into the
+Ultralytics framework. Original implementations:
+
+    BoT-SORT:  https://github.com/NirAharon/BoT-SORT
+    ByteTrack: https://github.com/FoundationVision/ByteTrack
+
+Users should consult the respective repositories for license terms.

README.md

@@ -1,5 +1,6 @@
 ---
-license: mit
+license: other
+license_name: intel-custom
 license_link: LICENSE
 library_name: openvino
 pipeline_tag: object-detection
@@ -10,9 +11,12 @@ tags:
   - yolo26
   - motion-tracking
   - multi-object-tracking
+  - bot-sort
   - edge-ai
   - metro
   - dlstreamer
+datasets:
+  - detection-datasets/coco
 language:
   - en
 ---
@@ -22,7 +26,7 @@ language:
 | Property | Value |
 |---|---|
 | **Category** | Object Detection + Multi-Object Tracking |
-| **Base Model** | [YOLO26](https://docs.ultralytics.com/models/yolo26/) (Ultralytics) + DLStreamer `gvatrack` (Kalman filter tracker) |
+| **Base Model** | [YOLO26](https://docs.ultralytics.com/models/yolo26/) (Ultralytics) + [BoT-SORT](https://github.com/NirAharon/BoT-SORT) tracker |
 | **Source Framework** | PyTorch (Ultralytics) |
 | **Supported Precisions** | FP32, FP16, INT8 (mixed-precision) |
 | **Inference Engine** | OpenVINO |
@@ -51,8 +55,7 @@ Typical Metro deployments include:
 
 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).
+The default tracker is BoT-SORT; ByteTrack is available as an alternative with lower computational overhead.
 
 ---
 
@@ -151,17 +154,15 @@ polylines are drawn with OpenCV, and the result is muxed to `output_dlstreamer.m
 import subprocess
 from collections import defaultdict
 
+import cv2
 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([])
+from gi.repository import Gst
+from gstgva import VideoFrame
 
-# Import cv2 after Gst.init to avoid GStreamer re-initialization conflicts.
-import cv2
+Gst.init(None)
 
 # For CPU: change device=GPU to device=CPU.
 # For NPU: change device=GPU to device=NPU (batch-size=1, nireq=4 recommended).
@@ -211,34 +212,16 @@ while True:
             stdin=subprocess.PIPE, stderr=subprocess.DEVNULL,
         )
 
-    # Read detection / tracking metadata via GstAnalytics.
-    rmeta = GstAnalytics.buffer_get_analytics_relation_meta(buf)
+    # Read detection / tracking metadata.
+    frame = VideoFrame(buf, caps=caps)
     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))
+    for region in frame.regions():
+        tid = region.object_id()
+        label = region.label()
+        rect = region.rect()
+        cx = int(rect.x + rect.w / 2)
+        cy = int(rect.y + rect.h / 2)
+        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)
@@ -282,14 +265,16 @@ print("Wrote output_dlstreamer.mp4", flush=True)
 
 ## License
 
+Copyright (C) Intel Corporation. All rights reserved.
 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/)
+- [BoT-SORT Tracker](https://github.com/NirAharon/BoT-SORT)
+- [ByteTrack Tracker](https://github.com/FoundationVision/ByteTrack)
 - [Intel DLStreamer gvatrack](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/elements/gvatrack.html)
-- [DLStreamer Object Tracking Guide](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/dev_guide/object_tracking.html)
 - [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)

export_and_quantize.sh

@@ -33,22 +33,11 @@ fi
 
 echo "--- Installing dependencies ---"
 if [[ "${PRECISION}" == "INT8" ]]; then
-    pip install -qU openvino nncf ultralytics
+    pip install -qU "openvino>=2026.0.0" "nncf>=3.0.0" ultralytics
 else
-    pip install -qU openvino ultralytics
+    pip install -qU "openvino>=2026.0.0" ultralytics
 fi
 
-# Ask for approval before downloading models and sample files
-echo ""
-echo "This script will download:"
-echo "  - Model weights and/or sample files"
-echo ""
-read -p "Continue with downloads? (yes/no): " APPROVAL
-if [[ "${APPROVAL}" != "yes" ]]; then
-    echo "Download cancelled by user."
-    exit 0
-fi
-echo ""
 echo "--- Downloading sample test video ---"
 if [[ ! -f test_video.mp4 ]]; then
     wget -q -O test_video.mp4 \
@@ -58,17 +47,6 @@ else
     echo "Already present: test_video.mp4"
 fi
 
-# Ask for approval before downloading models and sample files
-echo ""
-echo "This script will download:"
-echo "  - Model weights and/or sample files"
-echo ""
-read -p "Continue with downloads? (yes/no): " APPROVAL
-if [[ "${APPROVAL}" != "yes" ]]; then
-    echo "Download cancelled by user."
-    exit 0
-fi
-echo ""
 echo "--- Downloading sample test image ---"
 if [[ ! -f test.jpg ]]; then
     wget -q -O test.jpg https://ultralytics.com/images/bus.jpg