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Autotracking tweaks and docs update (#8345)
* refactor thresholds and reduce a duplicate call * add camera to docs * udpate docs
This commit is contained in:
Josh Hawkins
GitHub
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a399cb09fa
@ -160,3 +160,7 @@ This is often caused by the same reason as above - the `MoveStatus` ONVIF parame
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### I'm seeing this error in the logs: "Autotracker: motion estimator couldn't get transformations". What does this mean?
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To maintain object tracking during PTZ moves, Frigate tracks the motion of your camera based on the details of the frame. If you are seeing this message, it could mean that your `zoom_factor` may be set too high, the scene around your detected object does not have enough details (like hard edges or color variatons), or your camera's shutter speed is too slow and motion blur is occurring. Try reducing `zoom_factor`, finding a way to alter the scene around your object, or changing your camera's shutter speed.
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### Calibration seems to have completed, but the camera is not actually moving to track my object. Why?
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Some cameras have firmware that reports that FOV RelativeMove, the ONVIF command that Frigate uses for autotracking, is supported. However, if the camera does not pan or tilt when an object comes into the required zone, your camera's firmware does not actually support FOV RelativeMove. One such camera is the Uniview IPC672LR-AX4DUPK. It actually moves its zoom motor instead of panning and tilting and does not follow the ONVIF standard whatsoever.
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@ -93,4 +93,5 @@ This list of working and non-working PTZ cameras is based on user feedback.
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| Sunba 405-D20X | ✅ | ❌ | |
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| Tapo C200 | ✅ | ❌ | Incomplete ONVIF support |
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| Tapo C210 | ❌ | ❌ | Incomplete ONVIF support |
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| Uniview IPC672LR-AX4DUPK | ✅ | ❌ | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands |
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| Vikylin PTZ-2804X-I2 | ❌ | ❌ | Incomplete ONVIF support |
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@ -502,7 +502,7 @@ class PtzAutoTracker:
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return np.dot(self.move_coefficients[camera], input_data)
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def _calculate_tracked_object_metrics(self, camera):
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def _calculate_tracked_object_metrics(self, camera, obj):
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def remove_outliers(data):
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Q1 = np.percentile(data, 25)
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Q3 = np.percentile(data, 75)
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@ -541,6 +541,27 @@ class PtzAutoTracker:
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"original_target_box"
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] = self.tracked_object_metrics[camera]["target_box"]
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(
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self.tracked_object_metrics[camera]["valid_velocity"],
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self.tracked_object_metrics[camera]["velocity"],
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) = self._get_valid_velocity(camera, obj)
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self.tracked_object_metrics[camera]["distance"] = self._get_distance_threshold(
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camera, obj
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)
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centroid_distance = np.linalg.norm(
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[
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obj.obj_data["centroid"][0] - camera_config.detect.width / 2,
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obj.obj_data["centroid"][1] - camera_config.detect.height / 2,
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]
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)
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logger.debug(f"{camera}: Centroid distance: {centroid_distance}")
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self.tracked_object_metrics[camera]["below_distance_threshold"] = (
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centroid_distance < self.tracked_object_metrics[camera]["distance"]
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)
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def _process_move_queue(self, camera):
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camera_config = self.config.cameras[camera]
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camera_config.frame_shape[1]
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@ -668,12 +689,16 @@ class PtzAutoTracker:
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velocities = obj.obj_data["estimate_velocity"]
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logger.debug(f"{camera}: Velocities from norfair: {velocities}")
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# if we are close enough to zero, return right away
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if np.all(np.round(velocities) == 0):
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return True, np.zeros((2, 2))
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# Thresholds
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x_mags_thresh = camera_width / camera_fps / 2
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y_mags_thresh = camera_height / camera_fps / 2
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dir_thresh = 0.96
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delta_thresh = 10
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var_thresh = 3
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dir_thresh = 0.93
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delta_thresh = 12
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var_thresh = 5
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# Check magnitude
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x_mags = np.abs(velocities[:, 0])
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@ -690,10 +715,14 @@ class PtzAutoTracker:
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high_variances = np.any(stdevs > var_thresh)
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# Check direction difference
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velocities = np.round(velocities)
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invalid_dirs = False
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if not np.any(np.linalg.norm(velocities, axis=1)):
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cosine_sim = np.dot(velocities[0], velocities[1]) / (
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np.linalg.norm(velocities[0]) * np.linalg.norm(velocities[1])
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)
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dir_thresh = 0.6 if np.all(delta < delta_thresh / 2) else dir_thresh
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print(f"cosine sim: {cosine_sim}")
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invalid_dirs = cosine_sim < dir_thresh
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# Combine
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@ -723,11 +752,12 @@ class PtzAutoTracker:
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)
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)
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# invalid velocity
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return np.zeros((2, 2))
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return False, np.zeros((2, 2))
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else:
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return np.mean(velocities, axis=0)
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logger.debug(f"{camera}: Valid velocity ")
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return True, np.mean(velocities, axis=0)
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def _below_distance_threshold(self, camera, obj):
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def _get_distance_threshold(self, camera, obj):
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# Returns true if Euclidean distance from object to center of frame is
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# less than 10% of the of the larger dimension (width or height) of the frame,
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# multiplied by a scaling factor for object size.
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@ -738,13 +768,6 @@ class PtzAutoTracker:
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# TODO: there's probably a better way to approach this
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camera_config = self.config.cameras[camera]
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centroid_distance = np.linalg.norm(
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[
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obj.obj_data["centroid"][0] - camera_config.detect.width / 2,
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obj.obj_data["centroid"][1] - camera_config.detect.height / 2,
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]
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)
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obj_width = obj.obj_data["box"][2] - obj.obj_data["box"][0]
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obj_height = obj.obj_data["box"][3] - obj.obj_data["box"][1]
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@ -758,14 +781,17 @@ class PtzAutoTracker:
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# larger objects should lower the threshold, smaller objects should raise it
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scaling_factor = 1 - np.log(max_obj / max_frame)
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percentage = 0.1 if camera_config.onvif.autotracking.movement_weights else 0.03
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percentage = (
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0.08
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if camera_config.onvif.autotracking.movement_weights
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and self.tracked_object_metrics[camera]["valid_velocity"]
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else 0.03
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)
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distance_threshold = percentage * max_frame * scaling_factor
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logger.debug(
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f"{camera}: Distance: {centroid_distance}, threshold: {distance_threshold}"
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)
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logger.debug(f"{camera}: Distance threshold: {distance_threshold}")
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return centroid_distance < distance_threshold
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return distance_threshold
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def _should_zoom_in(self, camera, obj, box, debug_zooming=False):
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# returns True if we should zoom in, False if we should zoom out, None to do nothing
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@ -774,7 +800,7 @@ class PtzAutoTracker:
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camera_height = camera_config.frame_shape[0]
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camera_fps = camera_config.detect.fps
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average_velocity = self._get_valid_velocity(camera, obj)
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average_velocity = self.tracked_object_metrics[camera]["velocity"]
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bb_left, bb_top, bb_right, bb_bottom = box
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@ -799,7 +825,9 @@ class PtzAutoTracker:
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)
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# make sure object is centered in the frame
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below_distance_threshold = self._below_distance_threshold(camera, obj)
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below_distance_threshold = self.tracked_object_metrics[camera][
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"below_distance_threshold"
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]
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below_dimension_threshold = (bb_right - bb_left) <= camera_width * (
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self.zoom_factor[camera] + 0.1
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@ -925,7 +953,14 @@ class PtzAutoTracker:
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): # use estimates if we have available coefficients
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predicted_movement_time = self._predict_movement_time(camera, pan, tilt)
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average_velocity = self._get_valid_velocity(camera, obj)
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_, average_velocity = (
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self._get_valid_velocity(camera, obj)
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if "velocity" not in self.tracked_object_metrics[camera]
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else (
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self.tracked_object_metrics[camera]["valid_velocity"],
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self.tracked_object_metrics[camera]["velocity"],
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)
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)
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if np.any(average_velocity):
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# this box could exceed the frame boundaries if velocity is high
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@ -992,7 +1027,7 @@ class PtzAutoTracker:
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level = (
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self.ptz_metrics[camera]["ptz_max_zoom"].value
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- self.ptz_metrics[camera]["ptz_min_zoom"].value
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) / 10
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) / 20
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if result:
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zoom = min(1.0, current_zoom_level + level)
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else:
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@ -1103,9 +1138,9 @@ class PtzAutoTracker:
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)
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):
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self.tracked_object_history[camera].append(copy.deepcopy(obj.obj_data))
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self._calculate_tracked_object_metrics(camera)
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self._calculate_tracked_object_metrics(camera, obj)
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if self._below_distance_threshold(camera, obj):
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if self.tracked_object_metrics[camera]["below_distance_threshold"]:
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logger.debug(
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f"{camera}: Existing object (do NOT move ptz): {obj.obj_data['id']} {obj.obj_data['box']} {obj.obj_data['frame_time']}"
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)
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@ -1148,7 +1183,7 @@ class PtzAutoTracker:
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self.tracked_object_history[camera].append(
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copy.deepcopy(obj.obj_data)
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)
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self._calculate_tracked_object_metrics(camera)
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self._calculate_tracked_object_metrics(camera, obj)
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self._autotrack_move_ptz(camera, obj)
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return
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