diff --git a/deep_sort/linear_assignment.py b/deep_sort/linear_assignment.py
index 178456cf..c095bbc7 100644
--- a/deep_sort/linear_assignment.py
+++ b/deep_sort/linear_assignment.py
@@ -1,7 +1,7 @@
 # vim: expandtab:ts=4:sw=4
 from __future__ import absolute_import
 import numpy as np
-from sklearn.utils.linear_assignment_ import linear_assignment
+from scipy.optimize import linear_sum_assignment as linear_assignment
 from . import kalman_filter
 
 
@@ -56,6 +56,7 @@ def min_cost_matching(
         tracks, detections, track_indices, detection_indices)
     cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5
     indices = linear_assignment(cost_matrix)
+    indices = np.hstack([indices[0].reshape(((indices[0].shape[0]), 1)),indices[1].reshape(((indices[0].shape[0]), 1))])
 
     matches, unmatched_tracks, unmatched_detections = [], [], []
     for col, detection_idx in enumerate(detection_indices):
diff --git a/tools/generate_detections.py b/tools/generate_detections.py
index c7192c26..6ac56a2d 100644
--- a/tools/generate_detections.py
+++ b/tools/generate_detections.py
@@ -72,15 +72,15 @@ class ImageEncoder(object):
 
     def __init__(self, checkpoint_filename, input_name="images",
                  output_name="features"):
-        self.session = tf.Session()
-        with tf.gfile.GFile(checkpoint_filename, "rb") as file_handle:
-            graph_def = tf.GraphDef()
+        self.session = tf.compat.v1.Session()
+        with tf.compat.v1.gfile.GFile(checkpoint_filename, "rb") as file_handle:
+            graph_def = tf.compat.v1.GraphDef()
             graph_def.ParseFromString(file_handle.read())
-        tf.import_graph_def(graph_def, name="net")
-        self.input_var = tf.get_default_graph().get_tensor_by_name(
-            "net/%s:0" % input_name)
-        self.output_var = tf.get_default_graph().get_tensor_by_name(
-            "net/%s:0" % output_name)
+        tf.compat.v1.import_graph_def(graph_def, name="net")
+        self.input_var = tf.compat.v1.get_default_graph().get_tensor_by_name(
+            "%s:0" % input_name) #"net/%s:0" % input_name
+        self.output_var = tf.compat.v1.get_default_graph().get_tensor_by_name(
+            "%s:0" % output_name) #"net/%s:0" % output_name
 
         assert len(self.output_var.get_shape()) == 2
         assert len(self.input_var.get_shape()) == 4