diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 801c58f7..27a08b76 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -172,26 +172,26 @@ class ByteTrack:
     </video>
 
     Parameters:
-        track_thresh (float, optional): Detection confidence threshold
+        track_activation_threshold (float, optional): Detection confidence threshold
             for track activation.
-        track_buffer (int, optional): Number of frames to buffer when a track is lost.
-        match_thresh (float, optional): Threshold for matching tracks with detections.
+        lost_track_buffer (int, optional): Number of frames to buffer when a track is lost.
+        minimum_matching_threshold (float, optional): Threshold for matching tracks with detections.
         frame_rate (int, optional): The frame rate of the video.
     """  # noqa: E501 // docs
 
     def __init__(
         self,
-        track_thresh: float = 0.25,
-        track_buffer: int = 30,
-        match_thresh: float = 0.8,
+        track_activation_threshold: float = 0.25,
+        lost_track_buffer: int = 30,
+        minimum_matching_threshold: float = 0.8,
         frame_rate: int = 30,
     ):
-        self.track_thresh = track_thresh
-        self.match_thresh = match_thresh
+        self.track_activation_threshold = track_activation_threshold
+        self.minimum_matching_threshold = minimum_matching_threshold
 
         self.frame_id = 0
-        self.det_thresh = self.track_thresh + 0.1
-        self.max_time_lost = int(frame_rate / 30.0 * track_buffer)
+        self.det_thresh = self.track_activation_threshold + 0.1
+        self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
         self.kalman_filter = KalmanFilter()
 
         self.tracked_tracks: List[STrack] = []
@@ -295,9 +295,9 @@ class ByteTrack:
         scores = tensors[:, 4]
         bboxes = tensors[:, :4]
 
-        remain_inds = scores > self.track_thresh
+        remain_inds = scores > self.track_activation_threshold
         inds_low = scores > 0.1
-        inds_high = scores < self.track_thresh
+        inds_high = scores < self.track_activation_threshold
 
         inds_second = np.logical_and(inds_low, inds_high)
         dets_second = bboxes[inds_second]
@@ -335,7 +335,7 @@ class ByteTrack:
 
         dists = matching.fuse_score(dists, detections)
         matches, u_track, u_detection = matching.linear_assignment(
-            dists, thresh=self.match_thresh
+            dists, thresh=self.minimum_matching_threshold
         )
 
         for itracked, idet in matches: