diff --git a/README.md b/README.md
index 36e9323..22afb2d 100644
--- a/README.md
+++ b/README.md
@@ -3,4 +3,6 @@
 
 Large-scale apriltag localisation for edge devices.
 
-See full demo [here](https://ftp.plios.tech/Docker%20AprilTag%20Localisation/AprilTagLocalisation.mp4).
\ No newline at end of file
+<img src="https://github.com/Sid220/cdn/blob/main/docker-apriltag-localisation/AprilTagLocalisationXS.gif">
+
+See full demo [here](https://ftp.plios.tech/Docker%20AprilTag%20Localisation/AprilTagLocalisation.mp4).
diff --git a/src/main.py b/src/main.py
index b50e52f..21bd531 100644
--- a/src/main.py
+++ b/src/main.py
@@ -116,17 +116,23 @@ def perform_localisation(img, mtx, dist, camera="unknown"):
             # https://github.com/Mechanical-Advantage/AdvantageKit/blob/ns-dev/akit/py/northstar/pipeline/CameraPoseEstimator.py#L98
             # field_to_camera_0 = field_to_tag_pose.transformBy(camera_to_tag_0.inverse())
             # field_to_camera_1 = field_to_tag_pose.transformBy(camera_to_tag_1.inverse())
-            # camera_to_tag_0 = np.dot(tvecs[0], cv2.Rodrigues(rvecs[0])[0])
-            # camera_to_tag_1 = np.dot(rvecs[1], cv2.Rodrigues(rvecs[1])[0])
-            # field_to_camera_translation_0 = np.dot(get_objp(ids[0][0]), np.linalg.inv(camera_to_tag_0))
-            # field_to_camera_rotation_0 = np.dot(get_objp(ids[0][0]), np.linalg.inv(rvecs[0]))
-            # field_to_camera_translation_1 = np.dot(get_objp(ids[0][0]), np.linalg.inv(camera_to_tag_1))
-            # field_to_camera_rotation_1 = np.dot(get_objp(ids[0][0]), np.linalg.inv(rvecs[1]))
 
-            translation_vectors.append(tvecs[0])
-            translation_vectors.append(tvecs[1])
+            camera_t_tag_0_R = cv2.Rodrigues(rvecs[0])[0]
+            camera_to_tag_0_trans = np.dot(camera_t_tag_0_R, tvecs[0])
+            camera_t_tag_0_pose = np.vstack([np.hstack([camera_t_tag_0_R, camera_to_tag_0_trans]),
+                                             [0, 0, 0, 1]])
+            camera_t_tag_1_R = cv2.Rodrigues(rvecs[1])[0]
+            camera_to_tag_1_trans = np.dot(camera_t_tag_1_R, tvecs[1])
+            camera_t_tag_1_pose = np.vstack([np.hstack([camera_t_tag_1_R, camera_to_tag_1_trans]),
+                                             [0, 0, 0, 1]])
 
-        except Exception as e:
+            field_to_camera_translation_0 = np.dot(np.linalg.inv(camera_t_tag_0_pose), get_objp(ids[0][0]))
+            field_to_camera_translation_1 = np.dot(np.linalg.inv(camera_t_tag_1_pose), get_objp(ids[0][0]))
+
+            translation_vectors.append(field_to_camera_translation_0[0].transpose())
+            translation_vectors.append(field_to_camera_translation_1[0].transpose())
+
+        except SyntaxError as e:
             # TODO: Handle this better
             print("Error in solvingPnP (one tag): " + str(e))
             return
@@ -142,18 +148,7 @@ def perform_localisation(img, mtx, dist, camera="unknown"):
         #
         #         translation_vectors.append(tvec)
         object_points = []
-        fake_object_points = []
         for tag_id in ids:
-            fid_width = tag_positions[tag_id[0]]["size"]["width"]
-            fid_height = tag_positions[tag_id[0]]["size"]["height"]
-            base_obj_points = np.array([[-fid_width / 2.0, fid_height / 2.0, 0.0],
-                                        [fid_width / 2.0, fid_height / 2.0, 0.0],
-                                        [fid_width / 2.0, -fid_height / 2.0, 0.0],
-                                        [-fid_width / 2.0, -fid_height / 2.0, 0.0]])
-            fake_object_points += [[-fid_width / 2.0, fid_height / 2.0, 0.0],
-                                   [fid_width / 2.0, fid_height / 2.0, 0.0],
-                                   [fid_width / 2.0, -fid_height / 2.0, 0.0],
-                                   [-fid_width / 2.0, -fid_height / 2.0, 0.0]]
             object_points += (get_objp(tag_id[0])[0]).tolist()
         try:
             _, rvecs, tvecs, errors = cv2.solvePnPGeneric(np.array(object_points).astype("float32"),
@@ -166,10 +161,15 @@ def perform_localisation(img, mtx, dist, camera="unknown"):
             # camera_to_field = Transform3d(camera_to_field_pose.translation(), camera_to_field_pose.rotation())
             # field_to_camera = camera_to_field.inverse()
             # field_to_camera_pose = Pose3d(field_to_camera.translation(), field_to_camera.rotation())
-            # field_to_camera_translation = np.linalg.inv(tvecs[0])
-
-            translation_vectors.append(tvecs[0])
-        except Exception as e:
+            camera_t_field_R = cv2.Rodrigues(rvecs[0])[0]
+            camera_t_field_trans = np.dot(camera_t_field_R, tvecs[0])
+            camera_t_field_pose = np.vstack([np.hstack([camera_t_field_R, camera_t_field_trans]),
+                                             [0, 0, 0, 1]])
+            field_to_camera_translation = np.linalg.inv(camera_t_field_pose)
+
+            new_tvec = np.expand_dims(field_to_camera_translation[0:3, 3].transpose(), 1)
+            translation_vectors.append(new_tvec)
+        except SyntaxError as e:
             # TODO: Handle this better
             print("Error in solvingPnP (multitag): " + str(e))
             return