diff --git a/yolox/tracker/byte_tracker.py b/yolox/tracker/byte_tracker.py
index 2d004599..626c3a90 100644
--- a/yolox/tracker/byte_tracker.py
+++ b/yolox/tracker/byte_tracker.py
@@ -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=np.float64)
         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..2572c4d1 100644
--- a/yolox/tracker/matching.py
+++ b/yolox/tracker/matching.py
@@ -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=np.float64)
     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=np.float64),
+        np.ascontiguousarray(btlbrs, dtype=np.float64)
     )
 
     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=np.float64)
     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=np.float64)
     #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=np.float64)
     cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric))  # Nomalized features
     return cost_matrix