diff --git a/supervision/tracker/byte_tracker/basetrack.py b/supervision/tracker/byte_tracker/basetrack.py
index 079bab22..8ae511b7 100644
--- a/supervision/tracker/byte_tracker/basetrack.py
+++ b/supervision/tracker/byte_tracker/basetrack.py
@@ -1,8 +1,5 @@
 class BaseTrack:
     def __init__(self):
-        self.is_activated = False
-
-        self.score = 0
         self.start_frame = 0
         self.frame_id = 0
 
diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 362d9d07..37d3a854 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -50,6 +50,7 @@ class STrack(BaseTrack):
     ):
         super().__init__()
         self.state = TrackState.New
+        self.is_activated = False
 
         self._tlwh = np.asarray(tlwh, dtype=np.float32)
         self.kalman_filter = None
diff --git a/supervision/tracker/byte_tracker/matching.py b/supervision/tracker/byte_tracker/matching.py
index f791e518..4a95bc2c 100644
--- a/supervision/tracker/byte_tracker/matching.py
+++ b/supervision/tracker/byte_tracker/matching.py
@@ -1,10 +1,13 @@
-from typing import List, Tuple
+from typing import TYPE_CHECKING, List, Tuple
 
 import numpy as np
 from scipy.optimize import linear_sum_assignment
 
 from supervision.detection.utils import box_iou_batch
 
+if TYPE_CHECKING:
+    from supervision.tracker.byte_tracker.core import STrack
+
 
 def indices_to_matches(
     cost_matrix: np.ndarray, indices: np.ndarray, thresh: float
@@ -53,11 +56,11 @@ def iou_distance(atracks: List, btracks: List) -> np.ndarray:
     return cost_matrix
 
 
-def fuse_score(cost_matrix: np.ndarray, detections: List) -> np.ndarray:
+def fuse_score(cost_matrix: np.ndarray, stracks: List["STrack"]) -> np.ndarray:
     if cost_matrix.size == 0:
         return cost_matrix
     iou_sim = 1 - cost_matrix
-    det_scores = np.array([det.score for det in detections])
+    det_scores = np.array([strack.score for strack in stracks])
     det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
     fuse_sim = iou_sim * det_scores
     fuse_cost = 1 - fuse_sim