mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-02-07 11:45:24 +03:00
Separate object reduction to own function and reduce confidence of boxes on edge of region
This commit is contained in:
Nick Mowen
parent
e9376ca285
commit
c4507901f3
@ -3,6 +3,7 @@
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import datetime
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import logging
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import math
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from collections import defaultdict
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import cv2
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import numpy as np
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@ -15,6 +16,7 @@ from frigate.models import Event, Regions, Timeline
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from frigate.util.image import (
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area,
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calculate_region,
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clipped,
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intersection,
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intersection_over_union,
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yuv_region_2_bgr,
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@ -414,43 +416,6 @@ def get_cluster_region(frame_shape, min_region, cluster, boxes):
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)
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def get_consolidated_object_detections(detected_object_groups):
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"""Drop detections that overlap too much"""
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consolidated_detections = []
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for group in detected_object_groups.values():
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# if the group only has 1 item, skip
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if len(group) == 1:
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consolidated_detections.append(group[0])
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continue
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# sort smallest to largest by area
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sorted_by_area = sorted(group, key=lambda g: g[3])
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for current_detection_idx in range(0, len(sorted_by_area)):
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current_detection = sorted_by_area[current_detection_idx]
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current_label = current_detection[0]
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current_box = current_detection[2]
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overlap = 0
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for to_check_idx in range(
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min(current_detection_idx + 1, len(sorted_by_area)),
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len(sorted_by_area),
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):
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to_check = sorted_by_area[to_check_idx][2]
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intersect_box = intersection(current_box, to_check)
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# if 90% of smaller detection is inside of another detection, consolidate
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if intersect_box is not None and area(intersect_box) / area(
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current_box
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) > LABEL_CONSOLIDATION_MAP.get(
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current_label, LABEL_CONSOLIDATION_DEFAULT
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):
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overlap = 1
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break
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if overlap == 0:
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consolidated_detections.append(sorted_by_area[current_detection_idx])
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return consolidated_detections
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def get_startup_regions(
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frame_shape: tuple[int],
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region_min_size: int,
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@ -483,3 +448,90 @@ def get_startup_regions(
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)
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return regions
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def reduce_detections(
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frame_shape: tuple[int],
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all_detections: list[tuple[any]],
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) -> list[tuple[any]]:
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"""Take a list of detections and reduce overlaps to create a list of confident detections."""
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def reduce_overlapping_detections(detections: list[tuple[any]]) -> list[tuple[any]]:
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"""apply non-maxima suppression to suppress weak, overlapping bounding boxes."""
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detected_object_groups = defaultdict(lambda: [])
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for detection in detections:
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detected_object_groups[detection[0]].append(detection)
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selected_objects = []
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for group in detected_object_groups.values():
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# o[2] is the box of the object: xmin, ymin, xmax, ymax
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# apply max/min to ensure values do not exceed the known frame size
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boxes = [
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(
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o[2][0],
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o[2][1],
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o[2][2] - o[2][0],
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o[2][3] - o[2][1],
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)
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for o in group
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]
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# reduce confidences for objects that are on edge of region
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confidences = [o[1] / 2 if clipped(o, frame_shape) else o[1] for o in group]
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idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
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# add objects
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for index in idxs:
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index = index if isinstance(index, np.int32) else index[0]
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obj = group[index]
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selected_objects.append(obj)
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# set the detections list to only include top objects
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return selected_objects
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def get_consolidated_object_detections(detections: list[tuple[any]]):
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"""Drop detections that overlap too much."""
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detected_object_groups = defaultdict(lambda: [])
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for detection in detections:
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detected_object_groups[detection[0]].append(detection)
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consolidated_detections = []
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for group in detected_object_groups.values():
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# if the group only has 1 item, skip
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if len(group) == 1:
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consolidated_detections.append(group[0])
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continue
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# sort smallest to largest by area
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sorted_by_area = sorted(group, key=lambda g: g[3])
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for current_detection_idx in range(0, len(sorted_by_area)):
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current_detection = sorted_by_area[current_detection_idx]
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current_label = current_detection[0]
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current_box = current_detection[2]
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overlap = 0
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for to_check_idx in range(
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min(current_detection_idx + 1, len(sorted_by_area)),
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len(sorted_by_area),
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):
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to_check = sorted_by_area[to_check_idx][2]
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intersect_box = intersection(current_box, to_check)
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# if 90% of smaller detection is inside of another detection, consolidate
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if intersect_box is not None and area(intersect_box) / area(
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current_box
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) > LABEL_CONSOLIDATION_MAP.get(
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current_label, LABEL_CONSOLIDATION_DEFAULT
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):
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overlap = 1
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break
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if overlap == 0:
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consolidated_detections.append(
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sorted_by_area[current_detection_idx]
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)
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return consolidated_detections
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return get_consolidated_object_detections(
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reduce_overlapping_detections(all_detections)
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)
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@ -7,10 +7,8 @@ import signal
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import subprocess as sp
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import threading
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import time
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from collections import defaultdict
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import cv2
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import numpy as np
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from setproctitle import setproctitle
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from frigate.config import CameraConfig, DetectConfig, ModelConfig
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@ -39,12 +37,12 @@ from frigate.util.object import (
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get_cluster_candidates,
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get_cluster_region,
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get_cluster_region_from_grid,
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get_consolidated_object_detections,
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get_min_region_size,
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get_startup_regions,
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inside_any,
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intersects_any,
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is_object_filtered,
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reduce_detections,
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)
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from frigate.util.services import listen
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@ -688,50 +686,10 @@ def process_frames(
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)
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)
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#########
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# merge objects
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#########
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# group by name
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detected_object_groups = defaultdict(lambda: [])
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for detection in detections:
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detected_object_groups[detection[0]].append(detection)
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selected_objects = []
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for group in detected_object_groups.values():
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# apply non-maxima suppression to suppress weak, overlapping bounding boxes
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# o[2] is the box of the object: xmin, ymin, xmax, ymax
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# apply max/min to ensure values do not exceed the known frame size
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boxes = [
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(
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o[2][0],
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o[2][1],
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o[2][2] - o[2][0],
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o[2][3] - o[2][1],
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)
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for o in group
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]
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confidences = [o[1] for o in group]
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idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
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# add objects
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for index in idxs:
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index = index if isinstance(index, np.int32) else index[0]
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obj = group[index]
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selected_objects.append(obj)
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# set the detections list to only include top objects
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detections = selected_objects
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consolidated_detections = reduce_detections(frame_shape, detections)
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# if detection was run on this frame, consolidate
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if len(regions) > 0:
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# group by name
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detected_object_groups = defaultdict(lambda: [])
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for detection in detections:
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detected_object_groups[detection[0]].append(detection)
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consolidated_detections = get_consolidated_object_detections(
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detected_object_groups
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)
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tracked_detections = [
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d
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for d in consolidated_detections
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