diff --git a/grounded_sam2_tracking_demo.py b/grounded_sam2_tracking_demo.py
index ec8301f..566c4a3 100644
--- a/grounded_sam2_tracking_demo.py
+++ b/grounded_sam2_tracking_demo.py
@@ -107,18 +107,45 @@ elif masks.ndim == 4:
 Step 3: Register each object's positive points to video predictor with seperate add_new_points call
 """
 
-# sample the positive points from mask for each objects
-all_sample_points = sample_points_from_masks(masks=masks, num_points=10)
+PROMPT_TYPE_FOR_VIDEO = "box" # or "point"
 
-for object_id, (label, points) in enumerate(zip(OBJECTS, all_sample_points), start=1):
-    labels = np.ones((points.shape[0]), dtype=np.int32)
-    _, out_obj_ids, out_mask_logits = video_predictor.add_new_points(
-        inference_state=inference_state,
-        frame_idx=ann_frame_idx,
-        obj_id=object_id,
-        points=points,
-        labels=labels,
-    )
+assert PROMPT_TYPE_FOR_VIDEO in ["point", "box", "mask"], "SAM 2 video predictor only support point/box/mask prompt"
+
+# If you are using point prompts, we uniformly sample positive points based on the mask
+if PROMPT_TYPE_FOR_VIDEO == "point":
+    # sample the positive points from mask for each objects
+    all_sample_points = sample_points_from_masks(masks=masks, num_points=10)
+
+    for object_id, (label, points) in enumerate(zip(OBJECTS, all_sample_points), start=1):
+        labels = np.ones((points.shape[0]), dtype=np.int32)
+        _, out_obj_ids, out_mask_logits = video_predictor.add_new_points_or_box(
+            inference_state=inference_state,
+            frame_idx=ann_frame_idx,
+            obj_id=object_id,
+            points=points,
+            labels=labels,
+        )
+# Using box prompt
+elif PROMPT_TYPE_FOR_VIDEO == "box":
+    for object_id, (label, box) in enumerate(zip(OBJECTS, input_boxes), start=1):
+        _, out_obj_ids, out_mask_logits = video_predictor.add_new_points_or_box(
+            inference_state=inference_state,
+            frame_idx=ann_frame_idx,
+            obj_id=object_id,
+            box=box,
+        )
+# Using mask prompt is a more straightforward way
+elif PROMPT_TYPE_FOR_VIDEO == "mask":
+    for object_id, (label, mask) in enumerate(zip(OBJECTS, masks), start=1):
+        labels = np.ones((1), dtype=np.int32)
+        _, out_obj_ids, out_mask_logits = video_predictor.add_new_mask(
+            inference_state=inference_state,
+            frame_idx=ann_frame_idx,
+            obj_id=object_id,
+            mask=mask
+        )
+else:
+    raise NotImplementedError("SAM 2 video predictor only support point/box/mask prompts")
 
 
 """
diff --git a/grounded_sam2_tracking_demo_custom_video_input_gd1.5.py b/grounded_sam2_tracking_demo_custom_video_input_gd1.5.py
index ef95854..9999145 100644
--- a/grounded_sam2_tracking_demo_custom_video_input_gd1.5.py
+++ b/grounded_sam2_tracking_demo_custom_video_input_gd1.5.py
@@ -28,7 +28,7 @@ TEXT_PROMPT = "hippopotamus."
 OUTPUT_VIDEO_PATH = "./hippopotamus_tracking_demo.mp4"
 SOURCE_VIDEO_FRAME_DIR = "./custom_video_frames"
 SAVE_TRACKING_RESULTS_DIR = "./tracking_results"
-API_TOKEN_FOR_GD1_5 = "3491a2a256fb7ed01b2e757b713c4cb0"
+API_TOKEN_FOR_GD1_5 = "Your API token"
 PROMPT_TYPE_FOR_VIDEO = "box" # choose from ["point", "box", "mask"]
 
 """
diff --git a/grounded_sam2_tracking_demo_with_gd1.5.py b/grounded_sam2_tracking_demo_with_gd1.5.py
index 7cd42b5..13f0f7d 100644
--- a/grounded_sam2_tracking_demo_with_gd1.5.py
+++ b/grounded_sam2_tracking_demo_with_gd1.5.py
@@ -129,9 +129,9 @@ elif masks.ndim == 4:
 Step 3: Register each object's positive points to video predictor with seperate add_new_points call
 """
 
-PROMPT_TYPE_FOR_VIDEO = "mask" # or "point"
+PROMPT_TYPE_FOR_VIDEO = "box" # or "point"
 
-assert PROMPT_TYPE_FOR_VIDEO in ["point", "mask"]
+assert PROMPT_TYPE_FOR_VIDEO in ["point", "box", "mask"], "SAM 2 video predictor only support point/box/mask prompt"
 
 # If you are using point prompts, we uniformly sample positive points based on the mask
 if PROMPT_TYPE_FOR_VIDEO == "point":
@@ -140,13 +140,22 @@ if PROMPT_TYPE_FOR_VIDEO == "point":
 
     for object_id, (label, points) in enumerate(zip(OBJECTS, all_sample_points), start=1):
         labels = np.ones((points.shape[0]), dtype=np.int32)
-        _, out_obj_ids, out_mask_logits = video_predictor.add_new_points(
+        _, out_obj_ids, out_mask_logits = video_predictor.add_new_points_or_box(
             inference_state=inference_state,
             frame_idx=ann_frame_idx,
             obj_id=object_id,
             points=points,
             labels=labels,
         )
+# Using box prompt
+elif PROMPT_TYPE_FOR_VIDEO == "box":
+    for object_id, (label, box) in enumerate(zip(OBJECTS, input_boxes), start=1):
+        _, out_obj_ids, out_mask_logits = video_predictor.add_new_points_or_box(
+            inference_state=inference_state,
+            frame_idx=ann_frame_idx,
+            obj_id=object_id,
+            box=box,
+        )
 # Using mask prompt is a more straightforward way
 elif PROMPT_TYPE_FOR_VIDEO == "mask":
     for object_id, (label, mask) in enumerate(zip(OBJECTS, masks), start=1):
@@ -157,6 +166,9 @@ elif PROMPT_TYPE_FOR_VIDEO == "mask":
             obj_id=object_id,
             mask=mask
         )
+else:
+    raise NotImplementedError("SAM 2 video predictor only support point/box/mask prompts")
+
 
 
 """