diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index c77878cf9..bbe92a9cd 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -13,7 +13,7 @@
 class STrack(BaseTrack):
     shared_kalman = KalmanFilter()
 
-    def __init__(self, tlwh, score, class_ids):
+    def __init__(self, tlwh, score, class_ids, minimum_consecutive_frames):
         # wait activate
         self._tlwh = np.asarray(tlwh, dtype=np.float32)
         self.kalman_filter = None
@@ -23,6 +23,9 @@ def __init__(self, tlwh, score, class_ids):
         self.score = score
         self.class_ids = class_ids
         self.tracklet_len = 0
+        self.track_id = -1
+
+        self.minimum_consecutive_frames = minimum_consecutive_frames
 
     def predict(self):
         mean_state = self.mean.copy()
@@ -53,7 +56,6 @@ def multi_predict(stracks):
     def activate(self, kalman_filter, frame_id):
         """Start a new tracklet"""
         self.kalman_filter = kalman_filter
-        self.track_id = self.next_id()
         self.mean, self.covariance = self.kalman_filter.initiate(
             self.tlwh_to_xyah(self._tlwh)
         )
@@ -73,7 +75,7 @@ def re_activate(self, new_track, frame_id, new_id=False):
         self.state = TrackState.Tracked
         self.is_activated = True
         self.frame_id = frame_id
-        if new_id:
+        if new_id and (self.minimum_consecutive_frames == 1):
             self.track_id = self.next_id()
         self.score = new_track.score
 
@@ -97,6 +99,9 @@ def update(self, new_track, frame_id):
 
         self.score = new_track.score
 
+        if self.tracklet_len == self.minimum_consecutive_frames:
+            self.track_id = self.next_id()
+
     @property
     def tlwh(self):
         """Get current position in bounding box format `(top left x, top left y,
@@ -186,6 +191,10 @@ class ByteTrack:
             Increasing minimum_matching_threshold improves accuracy but risks fragmentation.
             Decreasing it improves completeness but risks false positives and drift.
         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.
+            Increasing minimum_consecutive_frames prevents the creation of accidental tracks from
+            false detection or double detection, but risks missing shorter tracks.
     """  # noqa: E501 // docs
 
     @deprecated_parameter(
@@ -218,6 +227,7 @@ def __init__(
         lost_track_buffer: int = 30,
         minimum_matching_threshold: float = 0.8,
         frame_rate: int = 30,
+        minimum_consecutive_frames: int = 1,
     ):
         self.track_activation_threshold = track_activation_threshold
         self.minimum_matching_threshold = minimum_matching_threshold
@@ -225,12 +235,16 @@ def __init__(
         self.frame_id = 0
         self.det_thresh = self.track_activation_threshold + 0.1
         self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
+        self.minimum_consecutive_frames = minimum_consecutive_frames
+
         self.kalman_filter = KalmanFilter()
 
         self.tracked_tracks: List[STrack] = []
         self.lost_tracks: List[STrack] = []
         self.removed_tracks: List[STrack] = []
 
+        BaseTrack.reset_counter()
+
     def update_with_detections(self, detections: Detections) -> Detections:
         """
         Updates the tracker with the provided detections and returns the updated
@@ -290,6 +304,7 @@ def callback(frame: np.ndarray, index: int) -> np.ndarray:
             return detections[detections.tracker_id != -1]
 
         else:
+            detections = Detections.empty()
             detections.tracker_id = np.array([], dtype=int)
 
             return detections
@@ -345,7 +360,7 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
         if len(dets) > 0:
             """Detections"""
             detections = [
-                STrack(STrack.tlbr_to_tlwh(tlbr), s, c)
+                STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
                 for (tlbr, s, c) in zip(dets, scores_keep, class_ids_keep)
             ]
         else:
@@ -387,7 +402,7 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
         if len(dets_second) > 0:
             """Detections"""
             detections_second = [
-                STrack(STrack.tlbr_to_tlwh(tlbr), s, c)
+                STrack(STrack.tlbr_to_tlwh(tlbr), s, c, self.minimum_consecutive_frames)
                 for (tlbr, s, c) in zip(dets_second, scores_second, class_ids_second)
             ]
         else:
@@ -458,7 +473,11 @@ def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
         self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks(
             self.tracked_tracks, self.lost_tracks
         )
-        output_stracks = [track for track in self.tracked_tracks if track.is_activated]
+        output_stracks = [
+            track
+            for track in self.tracked_tracks
+            if (track.is_activated and track.track_id >= 0)
+        ]
 
         return output_stracks