diff --git a/yolox/tracker/byte_tracker.py b/yolox/tracker/byte_tracker.py
index 2d004599..6aed9b71 100644
--- a/yolox/tracker/byte_tracker.py
+++ b/yolox/tracker/byte_tracker.py
@@ -7,7 +7,7 @@
 import torch.nn.functional as F
 
 from .kalman_filter import KalmanFilter
-from yolox.tracker import matching
+from ByteTrack.yolox.tracker import matching
 from .basetrack import BaseTrack, TrackState
 
 class STrack(BaseTrack):
@@ -15,7 +15,7 @@ class STrack(BaseTrack):
     def __init__(self, tlwh, score):
 
         # wait activate
-        self._tlwh = np.asarray(tlwh, dtype=np.float)
+        self._tlwh = np.asarray(tlwh, dtype=float)
         self.kalman_filter = None
         self.mean, self.covariance = None, None
         self.is_activated = False
diff --git a/yolox/tracker/matching.py b/yolox/tracker/matching.py
index d36a6cf5..8fdb5bc0 100644
--- a/yolox/tracker/matching.py
+++ b/yolox/tracker/matching.py
@@ -5,7 +5,7 @@
 from scipy.spatial.distance import cdist
 
 from cython_bbox import bbox_overlaps as bbox_ious
-from yolox.tracker import kalman_filter
+from ByteTrack.yolox.tracker import kalman_filter
 import time
 
 def merge_matches(m1, m2, shape):
@@ -58,13 +58,13 @@ def ious(atlbrs, btlbrs):
 
     :rtype ious np.ndarray
     """
-    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
+    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=float)
     if ious.size == 0:
         return ious
 
     ious = bbox_ious(
-        np.ascontiguousarray(atlbrs, dtype=np.float),
-        np.ascontiguousarray(btlbrs, dtype=np.float)
+        np.ascontiguousarray(atlbrs, dtype=float),
+        np.ascontiguousarray(btlbrs, dtype=float)
     )
 
     return ious
@@ -118,13 +118,13 @@ def embedding_distance(tracks, detections, metric='cosine'):
     :return: cost_matrix np.ndarray
     """
 
-    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
+    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=float)
     if cost_matrix.size == 0:
         return cost_matrix
-    det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
+    det_features = np.asarray([track.curr_feat for track in detections], dtype=float)
     #for i, track in enumerate(tracks):
         #cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
-    track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)
+    track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float)
     cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric))  # Nomalized features
     return cost_matrix
 
@@ -178,4 +178,4 @@ def fuse_score(cost_matrix, detections):
     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
-    return fuse_cost
\ No newline at end of file
+    return fuse_cost