From b380bd2df79a167ffd2b768896eda49eb048c7b5 Mon Sep 17 00:00:00 2001
From: LinasKo <linkondrackis@gmail.com>
Date: Fri, 18 Oct 2024 02:23:08 +0300
Subject: [PATCH] Correct but useless: Moved shared kalman to ByteTrack

* It's not stateful! All it keeps is the initial params - motion and update matrices
---
 supervision/tracker/byte_tracker/core.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index a1ae63f4..019ce890 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -37,13 +37,12 @@ class IdCounter:
 
 
 class STrack:
-    shared_kalman = KalmanFilter()
-
     def __init__(
         self,
         tlwh,
         score,
         minimum_consecutive_frames,
+        shared_kalman: KalmanFilter,
         internal_id_counter: IdCounter,
         external_id_counter: IdCounter,
     ):
@@ -54,6 +53,7 @@ class STrack:
 
         self._tlwh = np.asarray(tlwh, dtype=np.float32)
         self.kalman_filter = None
+        self.shared_kalman = shared_kalman
         self.mean, self.covariance = None, None
         self.is_activated = False
 
@@ -76,7 +76,7 @@ class STrack:
         )
 
     @staticmethod
-    def multi_predict(stracks):
+    def multi_predict(stracks, shared_kalman: KalmanFilter):
         if len(stracks) > 0:
             multi_mean = []
             multi_covariance = []
@@ -86,7 +86,7 @@ class STrack:
                 if st.state != TrackState.Tracked:
                     multi_mean[i][7] = 0
 
-            multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(
+            multi_mean, multi_covariance = shared_kalman.multi_predict(
                 np.asarray(multi_mean), np.asarray(multi_covariance)
             )
             for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
@@ -241,6 +241,7 @@ class ByteTrack:
         self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer)
         self.minimum_consecutive_frames = minimum_consecutive_frames
         self.kalman_filter = KalmanFilter()
+        self.shared_kalman = KalmanFilter()
 
         self.tracked_tracks: List[STrack] = []
         self.lost_tracks: List[STrack] = []
@@ -374,6 +375,7 @@ class ByteTrack:
                     STrack.tlbr_to_tlwh(tlbr),
                     s,
                     self.minimum_consecutive_frames,
+                    self.shared_kalman,
                     self.internal_id_counter,
                     self.external_id_counter,
                 )
@@ -395,7 +397,7 @@ class ByteTrack:
         """ Step 2: First association, with high score detection boxes"""
         strack_pool = joint_tracks(tracked_stracks, self.lost_tracks)
         # Predict the current location with KF
-        STrack.multi_predict(strack_pool)
+        STrack.multi_predict(strack_pool, self.shared_kalman)
         dists = matching.iou_distance(strack_pool, detections)
 
         dists = matching.fuse_score(dists, detections)
@@ -422,6 +424,7 @@ class ByteTrack:
                     STrack.tlbr_to_tlwh(tlbr),
                     s,
                     self.minimum_consecutive_frames,
+                    self.shared_kalman,
                     self.internal_id_counter,
                     self.external_id_counter,
                 )