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Drone detection #14

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Drone detection #14

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68d4fe5
no cls logic
hocjordan Feb 27, 2026
d280804
class
hocjordan Feb 27, 2026
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41 changes: 29 additions & 12 deletions bytetracker/byte_tracker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,7 +106,10 @@ def re_activate(self, new_track, frame_id, new_id=False):
if new_id:
self.track_id = self.next_id()
self.score = new_track.score
self.cls = new_track.cls
# Only update class if new class is not 5 (unknown/ambiguous)
# This preserves the known class when object becomes temporarily ambiguous
if new_track.cls != 5:
self.cls = new_track.cls

def update(self, new_track, frame_id):
"""
Expand All @@ -118,7 +121,6 @@ def update(self, new_track, frame_id):
"""
self.frame_id = frame_id
self.tracklet_len += 1
# self.cls = cls

new_tlwh = new_track.tlwh
self.mean, self.covariance = self.kalman_filter.update(
Expand All @@ -128,6 +130,10 @@ def update(self, new_track, frame_id):
self.is_activated = True

self.score = new_track.score
# Only update class if new class is not 5 (unknown/ambiguous)
# This preserves the known class when object becomes temporarily ambiguous
if new_track.cls != 5:
self.cls = new_track.cls

@property
# @jit(nopython=True)
Expand Down Expand Up @@ -186,7 +192,7 @@ def __repr__(self):

class BYTETracker(object):
def __init__(
self, track_thresh=0.45, track_buffer=25, match_thresh=0.8, frame_rate=30
self, track_thresh=0.45, track_buffer=25, match_thresh=0.8, frame_rate=30, det_thresh=None, second_match_thresh=None, unconfirmed_match_thresh=None
):
self.tracked_stracks = [] # type: list[STrack]
self.lost_stracks = [] # type: list[STrack]
Expand All @@ -198,11 +204,23 @@ def __init__(
self.track_thresh = track_thresh
self.match_thresh = match_thresh
# self.det_thresh = track_thresh
self.det_thresh = track_thresh + 0.1
if det_thresh is None:
self.det_thresh = track_thresh + 0.1
else:
self.det_thresh = det_thresh
# self.det_thresh = track_thresh + 0.1

# Make second association and unconfirmed track thresholds configurable
# Default to 0.5 for backward compatibility, but allow override for sensitive mode
self.second_match_thresh = second_match_thresh if second_match_thresh is not None else 0.5
self.unconfirmed_match_thresh = unconfirmed_match_thresh if unconfirmed_match_thresh is not None else 0.7

self.buffer_size = int(frame_rate / 30.0 * track_buffer)
self.max_time_lost = self.buffer_size
self.kalman_filter = KalmanFilter()

print(f"[Tracker] Initialized with track_thresh={track_thresh}, track_buffer={track_buffer}, match_thresh={match_thresh}, frame_rate={frame_rate}, det_thresh={self.det_thresh}, second_match_thresh={self.second_match_thresh}, unconfirmed_match_thresh={self.unconfirmed_match_thresh}")

def update(self, dets, _ = None):
self.frame_id += 1
activated_starcks = []
Expand Down Expand Up @@ -231,7 +249,7 @@ def update(self, dets, _ = None):
confs = confs

remain_inds = confs > self.track_thresh
inds_low = confs > 0.1
inds_low = confs > self.det_thresh
inds_high = confs < self.track_thresh

inds_second = np.logical_and(inds_low, inds_high)
Expand All @@ -243,7 +261,7 @@ def update(self, dets, _ = None):
scores_second = confs[inds_second]

clss_keep = classes[remain_inds]
clss_second = classes[remain_inds]
clss_second = classes[inds_second]

if len(dets) > 0:
"""Detections"""
Expand Down Expand Up @@ -300,7 +318,7 @@ def update(self, dets, _ = None):
]
dists = matching.iou_distance(r_tracked_stracks, detections_second)
matches, u_track, u_detection_second = matching.linear_assignment(
dists, thresh=0.5
dists, thresh=self.second_match_thresh
)
for itracked, idet in matches:
track = r_tracked_stracks[itracked]
Expand All @@ -324,7 +342,7 @@ def update(self, dets, _ = None):
# if not self.args.mot20:
dists = matching.fuse_score(dists, detections)
matches, u_unconfirmed, u_detection = matching.linear_assignment(
dists, thresh=0.7
dists, thresh=self.unconfirmed_match_thresh
)
for itracked, idet in matches:
unconfirmed[itracked].update(detections[idet], self.frame_id)
Expand Down Expand Up @@ -366,11 +384,10 @@ def update(self, dets, _ = None):
outputs = []
for t in output_stracks:
output = []
tlwh = t.tlwh
# Use tlbr property which correctly converts tlwh to xyxy format
# tlbr = top-left-bottom-right = [x1, y1, x2, y2]
xyxy = t.tlbr
tid = t.track_id
tlwh = np.expand_dims(tlwh, axis=0)
xyxy = xywh2xyxy(tlwh)
xyxy = np.squeeze(xyxy, axis=0)
output.extend(xyxy)
output.append(tid)
output.append(t.cls)
Expand Down