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Fix external track ids #1364

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Fix external track ids #1364

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104b5b8
fix exteranl track ids getting assigned too early
rolson24 Jul 5, 2024
6bf5174
Merge branch 'roboflow:develop' into fix_external_track_ids
rolson24 Jul 15, 2024
81b29f4
Fix external track IDs
rolson24 Jul 15, 2024
ad92ccb
Fixed docstring to correct minimum_matching_threshold explaination
rolson24 Jul 15, 2024
da24cba
Change overlap value for duplicate tracks to be much lower
rolson24 Jul 15, 2024
f0f9e00
Change overlap value for duplicate tracks to be 0.05
rolson24 Jul 15, 2024
64d5ecd
remove comment
rolson24 Jul 17, 2024
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11 changes: 5 additions & 6 deletions supervision/tracker/byte_tracker/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,9 +67,8 @@ def activate(self, kalman_filter, frame_id):
self.state = TrackState.Tracked
if frame_id == 1:
self.is_activated = True

if self.minimum_consecutive_frames == 1:
self.external_track_id = self.next_external_id()
if self.minimum_consecutive_frames == 1:
self.external_track_id = self.next_external_id()

self.frame_id = frame_id
self.start_frame = frame_id
Expand Down Expand Up @@ -206,8 +205,8 @@ class ByteTrack:
reducing the likelihood of track fragmentation or disappearance caused
by brief detection gaps.
minimum_matching_threshold (float, optional): Threshold for matching tracks with detections.
Increasing minimum_matching_threshold improves accuracy but risks fragmentation.
Decreasing it improves completeness but risks false positives and drift.
Decreasing minimum_matching_threshold improves accuracy but risks fragmentation.
Increasing it improves completeness but risks false positives and drift.
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frame_rate (int, optional): The frame rate of the video.
minimum_consecutive_frames (int, optional): Number of consecutive frames that an object must
be tracked before it is considered a 'valid' track.
Expand Down Expand Up @@ -545,7 +544,7 @@ def sub_tracks(track_list_a: List, track_list_b: List) -> List[int]:

def remove_duplicate_tracks(tracks_a: List, tracks_b: List) -> Tuple[List, List]:
pairwise_distance = matching.iou_distance(tracks_a, tracks_b)
matching_pairs = np.where(pairwise_distance < 0.15)
matching_pairs = np.where(pairwise_distance < 0.05)

duplicates_a, duplicates_b = set(), set()
for track_index_a, track_index_b in zip(*matching_pairs):
Expand Down