Sync motion-tracking from metro-analytics-catalog

LICENSE

@@ -43,3 +43,15 @@ Ultralytics and licensed under the GNU Affero General Public License v3.0
 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

@@ -10,7 +10,6 @@ tags:
   - yolo26
   - motion-tracking
   - multi-object-tracking
-  - bot-sort
   - edge-ai
   - metro
   - dlstreamer
@@ -25,7 +24,7 @@ language:
 | Property | Value |
 |---|---|
 | **Category** | Object Detection + Multi-Object Tracking |
-| **Base Model** | [YOLO26](https://docs.ultralytics.com/models/yolo26/) (Ultralytics) + [BoT-SORT](https://github.com/NirAharon/BoT-SORT) tracker |
+| **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 |
@@ -54,7 +53,8 @@ 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 BoT-SORT; ByteTrack is available as an alternative with lower computational overhead.
+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).
 
 ---
 
@@ -158,8 +158,8 @@ import numpy as np
 import gi
 
 gi.require_version("Gst", "1.0")
-from gi.repository import Gst
-from gstgva import VideoFrame
+gi.require_version("GstAnalytics", "1.0")
+from gi.repository import Gst, GLib, GstAnalytics
 
 Gst.init(None)
 
@@ -211,16 +211,34 @@ while True:
             stdin=subprocess.PIPE, stderr=subprocess.DEVNULL,
         )
 
-    # Read detection / tracking metadata.
-    frame = VideoFrame(buf, caps=caps)
+    # Read detection / tracking metadata via GstAnalytics.
+    rmeta = GstAnalytics.buffer_get_analytics_relation_meta(buf)
     regions_data = []
-    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))
+    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)
@@ -271,9 +289,8 @@ Licensed under the MIT License. See [LICENSE](LICENSE) for details.
 
 - [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)