Improve stationary classification (#20303)

* Improve stationary classification

* Cleanup for mypy

frigate/track/norfair_tracker.py

@@ -17,7 +17,11 @@ from frigate.camera import PTZMetrics
 from frigate.config import CameraConfig
 from frigate.ptz.autotrack import PtzMotionEstimator
 from frigate.track import ObjectTracker
-from frigate.track.stationary_classifier import StationaryMotionClassifier
+from frigate.track.stationary_classifier import (
+    StationaryMotionClassifier,
+    StationaryThresholds,
+    get_stationary_threshold,
+)
 from frigate.util.image import (
     SharedMemoryFrameManager,
     get_histogram,
@@ -28,12 +32,6 @@ from frigate.util.object import average_boxes, median_of_boxes
 logger = logging.getLogger(__name__)
 
 
-THRESHOLD_KNOWN_ACTIVE_IOU = 0.2
-THRESHOLD_STATIONARY_CHECK_IOU = 0.6
-THRESHOLD_ACTIVE_CHECK_IOU = 0.9
-MAX_STATIONARY_HISTORY = 10
-
-
 # Normalizes distance from estimate relative to object size
 # Other ideas:
 # - if estimates are inaccurate for first N detections, compare with last_detection (may be fine)
@@ -328,6 +326,7 @@ class NorfairTracker(ObjectTracker):
         id: str,
         box: list[int],
         stationary: bool,
+        thresholds: StationaryThresholds,
         yuv_frame: np.ndarray | None,
     ) -> bool:
         def reset_position(xmin: int, ymin: int, xmax: int, ymax: int) -> None:
@@ -346,9 +345,9 @@ class NorfairTracker(ObjectTracker):
         position = self.positions[id]
         self.stationary_box_history[id].append(box)
 
-        if len(self.stationary_box_history[id]) > MAX_STATIONARY_HISTORY:
+        if len(self.stationary_box_history[id]) > thresholds.max_stationary_history:
             self.stationary_box_history[id] = self.stationary_box_history[id][
-                -MAX_STATIONARY_HISTORY:
+                -thresholds.max_stationary_history :
             ]
 
         avg_box = average_boxes(self.stationary_box_history[id])
@@ -367,7 +366,7 @@ class NorfairTracker(ObjectTracker):
 
         # object has minimal or zero iou
         # assume object is active
-        if avg_iou < THRESHOLD_KNOWN_ACTIVE_IOU:
+        if avg_iou < thresholds.known_active_iou:
             if stationary and yuv_frame is not None:
                 if not self.stationary_classifier.evaluate(
                     id, yuv_frame, cast(tuple[int, int, int, int], tuple(box))
@@ -379,7 +378,9 @@ class NorfairTracker(ObjectTracker):
                 return False
 
         threshold = (
-            THRESHOLD_STATIONARY_CHECK_IOU if stationary else THRESHOLD_ACTIVE_CHECK_IOU
+            thresholds.stationary_check_iou
+            if stationary
+            else thresholds.active_check_iou
         )
 
         # object has iou below threshold, check median and optionally crop similarity
@@ -447,6 +448,7 @@ class NorfairTracker(ObjectTracker):
         self,
         track_id: str,
         obj: dict[str, Any],
+        thresholds: StationaryThresholds,
         yuv_frame: np.ndarray | None,
     ) -> None:
         id = self.track_id_map[track_id]
@@ -456,7 +458,7 @@ class NorfairTracker(ObjectTracker):
             >= self.detect_config.stationary.threshold
         )
         # update the motionless count if the object has not moved to a new position
-        if self.update_position(id, obj["box"], stationary, yuv_frame):
+        if self.update_position(id, obj["box"], stationary, thresholds, yuv_frame):
             self.tracked_objects[id]["motionless_count"] += 1
             if self.is_expired(id):
                 self.deregister(id, track_id)
@@ -502,9 +504,9 @@ class NorfairTracker(ObjectTracker):
         detections_by_type: dict[str, list[Detection]] = {}
         yuv_frame: np.ndarray | None = None
 
-        if self.ptz_metrics.autotracker_enabled.value or (
-            self.detect_config.stationary.classifier
-            and any(obj[0] == "car" for obj in detections)
+        if (
+            self.ptz_metrics.autotracker_enabled.value
+            or self.detect_config.stationary.classifier
         ):
             yuv_frame = self.frame_manager.get(
                 frame_name, self.camera_config.frame_shape_yuv
@@ -614,10 +616,12 @@ class NorfairTracker(ObjectTracker):
                     self.tracked_objects[id]["estimate"] = new_obj["estimate"]
             # else update it
             else:
+                thresholds = get_stationary_threshold(new_obj["label"])
                 self.update(
                     str(t.global_id),
                     new_obj,
-                    yuv_frame if new_obj["label"] == "car" else None,
+                    thresholds,
+                    yuv_frame if thresholds.motion_classifier_enabled else None,
                 )
 
         # clear expired tracks

frigate/track/stationary_classifier.py

@@ -1,6 +1,7 @@
 """Tools for determining if an object is stationary."""
 
 import logging
+from dataclasses import dataclass
 from typing import Any, cast
 
 import cv2
@@ -10,10 +11,60 @@ from scipy.ndimage import gaussian_filter
 logger = logging.getLogger(__name__)
 
 
-THRESHOLD_KNOWN_ACTIVE_IOU = 0.2
-THRESHOLD_STATIONARY_CHECK_IOU = 0.6
-THRESHOLD_ACTIVE_CHECK_IOU = 0.9
-MAX_STATIONARY_HISTORY = 10
+@dataclass
+class StationaryThresholds:
+    """IOU thresholds and history parameters for stationary object classification.
+
+    This allows different sensitivity settings for different object types.
+    """
+
+    # Objects to apply these thresholds to
+    # If None, apply to all objects
+    objects: list[str] = []
+
+    # Threshold of IoU that causes the object to immediately be considered active
+    # Below this threshold, assume object is active
+    known_active_iou: float = 0.2
+
+    # IOU threshold for checking if stationary object has moved
+    # If mean and median IOU drops below this, assume object is no longer stationary
+    stationary_check_iou: float = 0.6
+
+    # IOU threshold for checking if active object has changed position
+    # Higher threshold makes it more difficult for the object to be considered stationary
+    active_check_iou: float = 0.9
+
+    # Maximum number of bounding boxes to keep in stationary history
+    max_stationary_history: int = 10
+
+    # Whether to use the motion classifier
+    motion_classifier_enabled: bool = False
+
+
+# Thresholds for objects that are expected to be stationary
+STATIONARY_OBJECT_THRESHOLDS = StationaryThresholds(
+    objects=["bbq_grill", "package", "waste_bin"],
+    known_active_iou=0.0,
+    motion_classifier_enabled=True,
+)
+
+# Thresholds for objects that are active but can be stationary for longer periods of time
+DYNAMIC_OBJECT_THRESHOLDS = StationaryThresholds(
+    objects=["bicycle", "boat", "car", "motorcycle", "tractor", "truck"],
+    motion_classifier_enabled=True,
+)
+
+
+def get_stationary_threshold(label: str) -> StationaryThresholds:
+    """Get the stationary thresholds for a given object label."""
+
+    if label in STATIONARY_OBJECT_THRESHOLDS.objects:
+        return STATIONARY_OBJECT_THRESHOLDS
+
+    if label in DYNAMIC_OBJECT_THRESHOLDS.objects:
+        return DYNAMIC_OBJECT_THRESHOLDS
+
+    return StationaryThresholds()
 
 
 class StationaryMotionClassifier: