end tracked events before replacing replay camera state on a scene switch

- test the replay model rebind against two configured models

docker/main/rootfs/usr/local/bin/fix-ownership

@@ -105,6 +105,8 @@ if [[ -n "$sentinel" ]]; then
     mismatch_expr+=(-a ! -path "$sentinel")
 fi
 
+exec 3>&1
+
 for path in "$@"; do
     # An absent root is an incomplete sweep, not a finished one: /media/frigate
     # is not in the image, so a boot before the volume is mounted would
@@ -121,7 +123,18 @@ for path in "$@"; do
     # stale mount. Tolerate it rather than aborting under errexit, but never
     # read a failed scan as "nothing to do": that would record the sweep as
     # complete without having looked.
-    if ! count=$(find "$path" -name lost+found -prune -o "${mismatch_expr[@]}" -printf '.' 2>/dev/null | wc -c); then
+    # The walk prints nothing until it ends, so report every 30s through fd 3
+    # (stdout) while the count itself comes back through the substitution.
+    if ! count=$(find "$path" -name lost+found -prune -o "(" "${mismatch_expr[@]}" ")" -printf 'm\n' -o -printf '.\n' 2>/dev/null \
+        | awk -v path="$path" '
+            BEGIN { next_report = systime() + 30 }
+            $0 == "m" { mismatched++ }
+            systime() >= next_report {
+                printf "[INFO] fix-ownership: still scanning %s, %d entries checked, %d need changes\n", path, NR, mismatched > "/dev/fd/3"
+                fflush("/dev/fd/3")
+                next_report = systime() + 30
+            }
+            END { print mismatched + 0 }'); then
         swept_clean=0
         echo "[WARN] fix-ownership: could not scan ${path}; will retry on next boot"
         continue

docs/docs/configuration/non_root.md

@@ -71,13 +71,14 @@ Both the script and the boot sweep report progress, so you can tell a slow sweep
 
 ```
 [INFO] fix-ownership: scanning /media/frigate for ownership mismatches; this may take a while on large filesystems
+[INFO] fix-ownership: still scanning /media/frigate, 2481000 entries checked, 2390112 need changes
 [WARN] fix-ownership: adjusting ownership of 4823941 entries under /media/frigate
 [INFO] fix-ownership: /media/frigate 5% (241197/4823941 entries)
 [INFO] fix-ownership: /media/frigate 10% (482394/4823941 entries)
 [INFO] fix-ownership: finished /media/frigate in 12m 4s
 ```
 
-The scan has no percentage because the total isn't known until it finishes. Watch the boot sweep with `docker logs -f frigate`.
+The scan has no percentage because the total isn't known until it finishes, so it reports a running count every 30 seconds instead. Watch the boot sweep with `docker logs -f frigate`.
 
 Once the volumes are aligned, start Frigate normally. A file at `/config/.permissions_version` records what was done, so later boots skip the sweep unless you change `PUID`/`PGID`.
 

frigate/api/app.py

@@ -45,7 +45,7 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateTopic,
 )
-from frigate.const import REDACTED_CREDENTIAL_SENTINEL
+from frigate.const import REDACTED_CREDENTIAL_SENTINEL, REPLAY_CAMERA_PREFIX
 from frigate.ffmpeg_presets import FFMPEG_HWACCEL_VAAPI, _gpu_selector
 from frigate.genai import PROVIDERS, load_providers
 from frigate.jobs.media_sync import (
@@ -979,6 +979,17 @@ def _config_set_in_memory(request: Request, body: AppConfigSetBody) -> JSONRespo
                     status_code=400,
                 )
 
+            # an unresolvable scene would crash the threads that look up the model
+            detect_update = sections.get("detect")
+            if isinstance(detect_update, dict) and "scene" in detect_update:
+                try:
+                    config._resolve_camera_model(cam_name, detect_update["scene"])
+                except (TypeError, ValueError):
+                    return JSONResponse(
+                        content={"success": False, "message": "Invalid detect scene"},
+                        status_code=400,
+                    )
+
             for section_name, update in sections.items():
                 err = apply_section_update(camera_config, section_name, update)
                 if err is not None:
@@ -987,6 +998,11 @@ def _config_set_in_memory(request: Request, body: AppConfigSetBody) -> JSONRespo
                         status_code=400,
                     )
 
+            # the model is resolved again from the scene when the replay
+            # camera is recycled by the refresh below
+            if "detect" in sections and cam_name.startswith(REPLAY_CAMERA_PREFIX):
+                config.drop_camera_model(cam_name)
+
         # Publish ZMQ updates so processing threads pick up changes
         if body.update_topic and body.update_topic.startswith("config/cameras/"):
             _, _, camera, field = body.update_topic.split("/")

frigate/app.py

@@ -76,7 +76,7 @@ from frigate.models import (
 from frigate.notices import install_registry
 from frigate.notices.registry import NoticeRegistry
 from frigate.object_detection.base import ObjectDetectProcess
-from frigate.object_detection.util import detection_frame_size
+from frigate.object_detection.util import create_detection_input_shm
 from frigate.output.output import OutputProcess
 from frigate.ptz.autotrack import PtzAutoTracker
 from frigate.ptz.onvif import OnvifController
@@ -399,14 +399,7 @@ class FrigateApp:
             model = self.config.model_for_camera(name)
             model_cameras[model.scene].append(name)
 
-            try:
+            shm_in = create_detection_input_shm(name, self.config.models)
-                shm_in = UntrackedSharedMemory(
-                    name=name,
-                    create=True,
-                    size=detection_frame_size(model),
-                )
-            except FileExistsError:
-                shm_in = UntrackedSharedMemory(name=name)
 
             try:
                 shm_out = UntrackedSharedMemory(

frigate/camera/maintainer.py

@@ -16,7 +16,7 @@ from frigate.config.camera.updater import (
 )
 from frigate.const import REPLAY_CAMERA_PREFIX
 from frigate.models import Regions
-from frigate.object_detection.util import detection_frame_size
+from frigate.object_detection.util import create_detection_input_shm
 from frigate.util.builtin import empty_and_close_queue
 from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory
 from frigate.util.object import get_camera_regions_grid
@@ -127,13 +127,10 @@ class CameraMaintainer(threading.Thread):
                 max(model.width, model.height),
             )
 
+            create_detection_input_shm(name, self.config.models)
+
             try:
                 UntrackedSharedMemory(name=f"out-{name}", create=True, size=20 * 6 * 4)
-                UntrackedSharedMemory(
-                    name=name,
-                    create=True,
-                    size=detection_frame_size(model),
-                )
             except FileExistsError:
                 pass
 

frigate/camera/state.py

@@ -296,7 +296,8 @@ class CameraState:
         return frame_copy
 
     def finished(self, obj_id: str) -> None:
-        del self.tracked_objects[obj_id]
+        # state rebuilt after a resolution or scene change no longer has the object
+        self.tracked_objects.pop(obj_id, None)
 
     def on(self, event_type: str, callback: Callable[..., Any]) -> None:
         self.callbacks[event_type].append(callback)

frigate/object_detection/base.py

@@ -113,6 +113,23 @@ class AsyncLocalObjectDetector(BaseLocalDetector):
         return self.detect_api.receive_output()
 
 
+def map_input_frame(
+    frame_manager: SharedMemoryFrameManager, camera: str, model: ModelConfig
+) -> np.ndarray | None:
+    """Map a camera's detection input segment as this model's input tensor.
+
+    The segment is sized for the largest configured model, so it can be larger
+    than this model's input.
+    """
+    buf = frame_manager.write(camera)
+    shape = (1, model.height, model.width, 3)
+
+    if buf is None or len(buf) < model.height * model.width * 3:
+        return None
+
+    return np.ndarray(shape, dtype=np.uint8, buffer=buf)
+
+
 class DetectorRunner(FrigateProcess):
     def __init__(
         self,
@@ -154,14 +171,10 @@ class DetectorRunner(FrigateProcess):
                 connection_id = self.detection_queue.get(timeout=1)
             except queue.Empty:
                 continue
-            input_frame = frame_manager.get(
+            input_frame = map_input_frame(
+                frame_manager,
                 connection_id,
-                (
+                self.detector_config.model,  # type: ignore[arg-type]
-                    1,
-                    self.detector_config.model.height,  # type: ignore[union-attr]
-                    self.detector_config.model.width,  # type: ignore[union-attr]
-                    3,
-                ),
             )
 
             if input_frame is None:
@@ -227,14 +240,10 @@ class AsyncDetectorRunner(FrigateProcess):
                 continue
 
             assert self._frame_manager is not None
-            input_frame = self._frame_manager.get(
+            input_frame = map_input_frame(
+                self._frame_manager,
                 connection_id,
-                (
+                self.detector_config.model,  # type: ignore[arg-type]
-                    1,
-                    self.detector_config.model.height,  # type: ignore[union-attr]
-                    self.detector_config.model.width,  # type: ignore[union-attr]
-                    3,
-                ),
             )
 
             if input_frame is None:

frigate/object_detection/util.py

@@ -6,18 +6,39 @@ import threading
 from numpy import ndarray
 
 from frigate.detectors.detector_config import InputTensorEnum, ModelConfig
+from frigate.util.image import UntrackedSharedMemory
 
 
-def detection_frame_size(model: ModelConfig) -> int:
+def create_detection_input_shm(
-    """Get the shared memory size a camera needs to hand frames to a model.
+    camera: str, models: list[ModelConfig]
+) -> UntrackedSharedMemory:
+    """Create the shared memory a camera hands detection frames through.
+
+    It is sized for the largest model so a camera re-added under the same name
+    with another scene can reuse it. Detectors keep mappings of it, so it is
+    never unlinked while they run.
 
     Args:
-        model: The model the camera runs on
+        camera: Name of the camera
+        models: Every configured model
 
     Returns:
-        Size in bytes of one model input frame
+        The camera's detection input segment
     """
-    return model.height * model.width * 3
+    size = max(model.height * model.width * 3 for model in models)
+
+    try:
+        return UntrackedSharedMemory(name=camera, create=True, size=size)
+    except FileExistsError:
+        shm = UntrackedSharedMemory(name=camera)
+
+    if shm.size >= size:
+        return shm
+
+    # left by an earlier run with smaller models, so no detector maps it yet
+    shm.close()
+    shm.unlink()
+    return UntrackedSharedMemory(name=camera, create=True, size=size)
 
 
 class RequestStore:

frigate/ptz/autotrack.py

@@ -585,26 +585,36 @@ class PtzAutoTracker(threading.Thread):
 
     def _predict_movement_time(self, camera, pan, tilt):
         combined_movement = abs(pan) + abs(tilt)
-        input_data = np.array([self.intercept[camera], combined_movement])
+        input_data = np.array([1, combined_movement])
 
         return np.dot(self.move_coefficients[camera], input_data)
 
-    def _predict_area_after_time(self, camera, time):
-        return np.dot(
-            self.tracked_object_metrics[camera]["area_coefficients"],
-            [self.tracked_object_history[camera][-1]["frame_time"] + time],
-        )
-
     def _predict_target_box(self, camera, predicted_time):
-        target_box = self.tracked_object_metrics[camera]["target_box"]
+        tom = self.tracked_object_metrics[camera]
+        target_box = tom["target_box"]
 
         if not predicted_time:
             return target_box
 
-        frame_shape = self.config.cameras[camera].frame_shape
+        camera_config = self.config.cameras[camera]
+        left, top, right, bottom = self.tracked_object_history[camera][-1]["box"]
+        size = max(right - left, bottom - top)
 
-        return target_box + self._predict_area_after_time(camera, predicted_time) / (
+        if size <= 0:
-            frame_shape[0] * frame_shape[1]
+            return target_box
+
+        # the corner velocities diverge as the object approaches the camera
+        frames = camera_config.detect.fps * predicted_time
+        velocity = tom["velocity"]
+        predicted_size = max(
+            right - left + frames * (velocity[2] - velocity[0]),
+            bottom - top + frames * (velocity[3] - velocity[1]),
+            1,
+        )
+
+        # target_box is the squared size raised to the zoom factor
+        return target_box * (predicted_size / size) ** (
+            2 * camera_config.onvif.autotracking.zoom_factor
         )
 
     def _calculate_tracked_object_metrics(self, camera, obj):
@@ -660,26 +670,6 @@ class PtzAutoTracker(threading.Thread):
 
         filtered_areas = remove_outliers(areas) if len(areas) > 3 else areas
 
-        # Filter entries that are not touching the frame edge
-        filtered_areas_not_touching_edge = [
-            entry
-            for entry in filtered_areas
-            if self._touching_frame_edges(camera, entry["box"]) == 0
-        ]
-
-        # Calculate regression for area change predictions
-        if len(filtered_areas_not_touching_edge):
-            X = np.array(
-                [item["frame_time"] for item in filtered_areas_not_touching_edge]
-            )
-            y = np.array([item["area"] for item in filtered_areas_not_touching_edge])
-
-            tom["area_coefficients"] = np.linalg.lstsq(X.reshape(-1, 1), y, rcond=None)[
-                0
-            ]
-        else:
-            tom["area_coefficients"] = np.array([0])
-
         weights = np.arange(1, len(filtered_areas) + 1)
         weighted_area = np.average(
             [item["area"] for item in filtered_areas], weights=weights
@@ -744,6 +734,8 @@ class PtzAutoTracker(threading.Thread):
                     continue
 
                 else:
+                    pan_tilt_times = None
+
                     if (
                         camera_config.onvif.autotracking.zooming
                         == ZoomingModeEnum.relative
@@ -755,17 +747,28 @@ class PtzAutoTracker(threading.Thread):
 
                             await self._wait_until_stopped(camera, metrics)
 
+                            # _zoom_absolute resets the metrics timestamps
+                            pan_tilt_times = (
+                                metrics.start_time.value,
+                                metrics.stop_time.value,
+                            )
+
                         if zoom > 0 and metrics.zoom_level.value != zoom:
                             await self.onvif._zoom_absolute(camera, zoom, 1)
 
                     await self._wait_until_stopped(camera, metrics)
 
+                    start_time, stop_time = pan_tilt_times or (
+                        metrics.start_time.value,
+                        metrics.stop_time.value,
+                    )
+
                     if camera_config.onvif.autotracking.movement_weights:
                         logger.debug(
                             f"{camera}: Predicted movement time: {self._predict_movement_time(camera, pan, tilt)}"
                         )
                         logger.debug(
-                            f"{camera}: Actual movement time: {metrics.stop_time.value - metrics.start_time.value}"
+                            f"{camera}: Actual movement time: {stop_time - start_time}"
                         )
 
                     # save metrics for better estimate calculations
@@ -781,8 +784,8 @@ class PtzAutoTracker(threading.Thread):
                             {
                                 "pan": pan,
                                 "tilt": tilt,
-                                "start_timestamp": metrics.start_time.value,
+                                "start_timestamp": start_time,
-                                "end_timestamp": metrics.stop_time.value,
+                                "end_timestamp": stop_time,
                             }
                         )
 
@@ -845,7 +848,6 @@ class PtzAutoTracker(threading.Thread):
         # Thresholds
         x_mags_thresh = camera_width / camera_fps / 2
         y_mags_thresh = camera_height / camera_fps / 2
-        dir_thresh = 0.93
         delta_thresh = 20
         var_thresh = 10
 
@@ -863,24 +865,12 @@ class PtzAutoTracker(threading.Thread):
         stdev_list = np.std(velocities, axis=0)
         high_variances = np.any(stdev_list > var_thresh)
 
-        # Check direction difference
+        # the corners of an object moving toward or away from the camera move
+        # in different directions, so the delta bounds how far they may differ
         velocities = np.round(velocities)
-        invalid_dirs = False
-        if np.all(np.linalg.norm(velocities, axis=1)):
-            cosine_sim = np.dot(velocities[0], velocities[1]) / (
-                np.linalg.norm(velocities[0]) * np.linalg.norm(velocities[1])
-            )
-            dir_thresh = 0.6 if np.all(delta < delta_thresh / 2) else dir_thresh
-            invalid_dirs = cosine_sim < dir_thresh
 
         # Combine
-        invalid = (
+        invalid = invalid_x_mags or invalid_y_mags or invalid_delta or high_variances
-            invalid_x_mags
-            or invalid_y_mags
-            or invalid_dirs
-            or invalid_delta
-            or high_variances
-        )
 
         if invalid:
             logger.debug(
@@ -891,7 +881,6 @@ class PtzAutoTracker(threading.Thread):
                         for var_name, is_invalid in [
                             ("invalid_x_mags", invalid_x_mags),
                             ("invalid_y_mags", invalid_y_mags),
-                            ("invalid_dirs", invalid_dirs),
                             ("invalid_delta", invalid_delta),
                             ("high_variances", high_variances),
                         ]

frigate/test/http_api/test_http_app.py

@@ -98,6 +98,46 @@ class TestHttpApp(BaseTestHttp):
             assert response.status_code == 200
             assert app.frigate_config.cameras["front_door"].objects.track == ["person"]
 
+    @patch("frigate.detectors.detector_config.load_labels")
+    def test_config_set_in_memory_rebinds_replay_model_on_scene_change(
+        self, mock_labels
+    ):
+        mock_labels.return_value = {}
+        replay = "_replay_front_door"
+        self.minimal_config["models"] = [
+            {"devices": ["cpu"]},
+            {"scene": "thermal", "devices": ["openvino:CPU"], "width": 300},
+        ]
+        self.minimal_config["cameras"][replay] = self.minimal_config["cameras"].pop(
+            "front_door"
+        )
+        app = super().create_app()
+        app.config_publisher = Mock()
+        assert app.frigate_config.model_for_camera(replay).scene == "default"
+
+        with AuthTestClient(app) as client:
+            response = client.put(
+                "/config/set",
+                json={
+                    "requires_restart": 0,
+                    "skip_save": True,
+                    "update_topic": f"config/cameras/{replay}/detect",
+                    "config_data": {
+                        "cameras": {replay: {"detect": {"scene": "thermal"}}}
+                    },
+                },
+            )
+
+        assert response.status_code == 200
+        assert app.frigate_config.cameras[replay].detect.scene == "thermal"
+        assert app.frigate_config.model_for_camera(replay).scene == "thermal"
+        topics = [
+            call.args[0].update_type.name
+            for call in app.config_publisher.publish_update.call_args_list
+        ]
+        assert topics[0] == "detect"
+        assert topics[-1] == "refresh"
+
     ####################################################################################################################
     ###################################  Credential redaction sentinel  ################################################
     ####################################################################################################################

frigate/test/test_ptz_autotrack.py

@@ -11,11 +11,14 @@ KeyError on the autotracker thread or silently keep the wrong state:
   running its motion estimator for a camera that could not autotrack.
 """
 
+import asyncio
 import unittest
-from unittest.mock import MagicMock
+from unittest.mock import AsyncMock, MagicMock
+
+import numpy as np
 
 from frigate.camera import PTZMetrics
-from frigate.config import FrigateConfig
+from frigate.config import FrigateConfig, ZoomingModeEnum
 from frigate.config.camera.updater import CameraConfigUpdateEnum
 from frigate.ptz.autotrack import PtzAutoTracker, calculate_max_target_box
 
@@ -123,6 +126,118 @@ class TestAutotrackerDisable(unittest.TestCase):
         self.assertIs(payload, autotracking)
 
 
+class TestAutotrackerPrediction(unittest.TestCase):
+    def test_movement_time_uses_the_fitted_intercept_unchanged(self) -> None:
+        tracker = _make_tracker()
+        tracker.intercept = {CAMERA: 0.5}
+        tracker.move_coefficients = {CAMERA: [0.3, 0.8]}
+
+        self.assertAlmostEqual(tracker._predict_movement_time(CAMERA, 0.25, 0.25), 0.7)
+
+    def test_shrinking_box_keeps_a_positive_target_box(self) -> None:
+        # 60 px shrinking 6 px/frame reaches zero after 2 s at 5 fps
+        target_box = self._target_box_for((0, 0, 60, 60), [3, 3, -3, -3], 2.0)
+
+        self.assertGreater(target_box, 0)
+        self.assertTrue(np.isfinite(target_box))
+
+    def test_approaching_object_keeps_its_velocity(self) -> None:
+        # the box grows, so the corners move in opposite directions
+        tracker = _make_tracker()
+        obj = MagicMock()
+        obj.obj_data = {"estimate_velocity": np.array([[-3.0, -4.0], [3.0, 1.0]])}
+
+        valid, velocity = tracker._get_valid_velocity(CAMERA, obj)
+
+        self.assertTrue(valid)
+        self.assertEqual(list(velocity), [-3, -4, 3, 1])
+
+    def test_corners_that_disagree_too_much_are_rejected(self) -> None:
+        tracker = _make_tracker()
+        obj = MagicMock()
+        obj.obj_data = {"estimate_velocity": np.array([[-15.0, 0.0], [15.0, 0.0]])}
+
+        valid, velocity = tracker._get_valid_velocity(CAMERA, obj)
+
+        self.assertFalse(valid)
+        self.assertFalse(np.any(velocity))
+
+    def _target_box(self, velocity: list[float], predicted_time: float) -> float:
+        return self._target_box_for((900, 400, 1000, 600), velocity, predicted_time)
+
+    def _target_box_for(
+        self,
+        box: tuple[int, int, int, int],
+        velocity: list[float],
+        predicted_time: float,
+    ) -> float:
+        tracker = _make_tracker()
+        tracker.tracked_object_history = {CAMERA: [{"box": box}]}
+        tracker.tracked_object_metrics = {
+            CAMERA: {"target_box": 0.2, "velocity": np.array(velocity)}
+        }
+
+        return tracker._predict_target_box(CAMERA, predicted_time)
+
+    def test_target_box_grows_with_an_approaching_object(self) -> None:
+        # the 200px height gains 4px per frame at 5 fps for 1 second
+        zoom_factor = 0.3
+
+        self.assertAlmostEqual(
+            self._target_box([-1, -2, 1, 2], 1.0),
+            0.2 * (220 / 200) ** (2 * zoom_factor),
+        )
+
+    def test_target_box_is_unchanged_without_growth_or_time(self) -> None:
+        self.assertAlmostEqual(self._target_box([5, 0, 5, 0], 1.0), 0.2)
+        self.assertEqual(self._target_box([-1, -2, 1, 2], 0), 0.2)
+
+
+class TestAutotrackerMoveQueue(unittest.IsolatedAsyncioTestCase):
+    async def test_absolute_zoom_does_not_overwrite_the_pan_tilt_sample(self) -> None:
+        tracker = _make_tracker()
+        autotracking = tracker.config.cameras[CAMERA].onvif.autotracking
+        autotracking.zooming = ZoomingModeEnum.absolute
+        autotracking.calibrate_on_startup = True
+        metrics = tracker.ptz_metrics[CAMERA]
+
+        tracker.stop_event = MagicMock()
+        tracker.stop_event.is_set.side_effect = [False, True]
+        tracker.move_queues = {CAMERA: asyncio.Queue()}
+        tracker.move_queue_locks = {CAMERA: asyncio.Lock()}
+        tracker.intercept = {CAMERA: 0.5}
+        tracker.move_metrics = {CAMERA: []}
+        tracker._wait_until_stopped = AsyncMock()
+        tracker._calculate_move_coefficients = MagicMock()
+
+        async def move_relative(*_args) -> None:
+            metrics.start_time.value = 100.0
+            metrics.stop_time.value = 102.0
+
+        async def zoom_absolute(*_args) -> None:
+            metrics.start_time.value = 103.0
+            metrics.stop_time.value = 0
+
+        tracker.onvif._move_relative = AsyncMock(side_effect=move_relative)
+        tracker.onvif._zoom_absolute = AsyncMock(side_effect=zoom_absolute)
+
+        tracker.move_queues[CAMERA].put_nowait((50.0, 0.5, 0.5, 0.5))
+        await tracker._process_move_queue(CAMERA)
+
+        tracker.onvif._zoom_absolute.assert_awaited_once()
+        self.assertEqual(
+            tracker.move_metrics[CAMERA],
+            [
+                {
+                    "pan": 0.5,
+                    "tilt": 0.5,
+                    "start_timestamp": 100.0,
+                    "end_timestamp": 102.0,
+                }
+            ],
+        )
+
+
 class TestMaxTargetBox(unittest.TestCase):
     def test_follows_zoom_factor(self) -> None:
         self.assertAlmostEqual(calculate_max_target_box(0.5), 0.6**2)

frigate/track/object_processing.py

@@ -91,6 +91,7 @@ class TrackedObjectProcessor(threading.Thread):
                 CameraConfigUpdateEnum.enabled,
                 CameraConfigUpdateEnum.motion,
                 CameraConfigUpdateEnum.objects,
+                CameraConfigUpdateEnum.refresh,
                 CameraConfigUpdateEnum.remove,
                 CameraConfigUpdateEnum.timestamp_style,
                 CameraConfigUpdateEnum.zones,
@@ -784,6 +785,21 @@ class TrackedObjectProcessor(threading.Thread):
 
                 self.requestor.send_data(UPDATE_CAMERA_ACTIVITY, self.camera_activity)
 
+            # a replay camera recycled onto another scene's model needs new state
+            for camera in updated_topics.get("refresh", []):
+                camera_state = self.camera_states.get(camera)
+
+                if (
+                    camera_state is None
+                    or camera_state.model is self.config.model_for_camera(camera)
+                ):
+                    continue
+
+                self.force_end_all_events(camera, camera_state)
+                camera_state.shutdown()
+                self.frame_manager.close_camera(camera)
+                self.create_camera_state(camera)
+
             # manage camera disabled state
             for camera, config in self.config.cameras.items():
                 if not config.enabled_in_config:

web/src/components/config-form/section-configs/detect.ts

@@ -240,14 +240,14 @@ const detect: SectionConfigOverrides = {
   },
   replay: {
     restartRequired: [],
-    fieldOrder: ["width", "height", "fps"],
+    fieldOrder: ["scene", "width", "height", "fps"],
     fieldGroups: {
       resolution: ["width", "height", "fps"],
     },
     hiddenFields: [
       "enabled",
       "enabled_in_config",
-      "scene",
+      hideSceneWithoutChoice,
       "min_initialized",
       "max_disappeared",
       "annotation_offset",

web/src/components/overlay/detail/TrackingDetails.tsx

@@ -150,17 +150,18 @@ export function TrackingDetails({
 
   // Fetch the VOD manifest JSON to get the actual clipFrom after keyframe
   // snapping. The backend may snap clipFrom backwards to a keyframe, making
-  // the video start earlier than the requested time.
+  // the video start earlier than the requested time. Uses sourceOffsetRef so
+  // the manifest describes the same clip as videoSource.
   const vodManifestUrl = useMemo(() => {
     if (!event.camera) return null;
-    const startTime =
+    const sourceOffset = sourceOffsetRef.current;
-      event.start_time + annotationOffset / 1000 - REVIEW_PADDING;
+    const startTime = event.start_time + sourceOffset / 1000 - REVIEW_PADDING;
     const endTime =
       (event.end_time ?? Date.now() / 1000) +
-      annotationOffset / 1000 +
+      sourceOffset / 1000 +
       REVIEW_PADDING;
     return `vod/clip/${event.camera}/start/${startTime}/end/${endTime}`;
-  }, [event, annotationOffset]);
+  }, [event]);
 
   const { data: vodManifest } = useSWR<VodManifest>(vodManifestUrl, null, {
     revalidateOnFocus: false,