diff --git a/README.md b/README.md
index cb798a8..096a5f9 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,7 @@ Grounded SAM 2 does not introduce significant methodological changes compared to
   - [Grounded SAM 2 Video Object Tracking Demo (with Grounding DINO 1.5 & 1.6)](#grounded-sam-2-video-object-tracking-demo-with-grounding-dino-15--16)
   - [Grounded SAM 2 Video Object Tracking with Custom Video Input (using Grounding DINO)](#grounded-sam-2-video-object-tracking-demo-with-custom-video-input-with-grounding-dino)
   - [Grounded SAM 2 Video Object Tracking with Custom Video Input (using Grounding DINO 1.5 & 1.6)](#grounded-sam-2-video-object-tracking-demo-with-custom-video-input-with-grounding-dino-15--16)
+  - [Grounded SAM 2 Video Object Tracking with Continues ID (using Grounding DINO)](#grounded-sam-2--video-object-tracking-with-continuous-id-with-grounding-dino)
 - [Citation](#citation)
 
 
@@ -167,7 +168,10 @@ And we will automatically save the tracking visualization results in `OUTPUT_VID
 > [!WARNING]
 > We initialize the box prompts on the first frame of the input video. If you want to start from different frame, you can refine `ann_frame_idx` by yourself in our code.
 
-### Grounded-SAM-2  Video Object Tracking with Continuous ID (with Grounding DINO)
+### Grounded-SAM-2 Video Object Tracking with Continuous ID (with Grounding DINO)
+
+In above demos, we only prompt Grounded SAM 2 in specific frame, which may not be friendly to find new object during the whole video. In this demo, we try to **find new objects** and assign them with new ID across the whole video, this function is **still under develop**. it's not that stable now.
+
 Users can upload their own video files and specify custom text prompts for grounding and tracking using the Grounding DINO and SAM 2 frameworks. To do this, execute the script:
 
 
@@ -186,12 +190,8 @@ You can customize various parameters including:
 - `text_threshold`: text threshold for groundingdino model
 Note: This method supports only the mask type of text prompt.
 
-The demo video is:
-[![car tracking demo data](./assets/tracking_car_1.jpg)](./assets/tracking_car.mp4)
-
-
 After running our demo code, you can get the tracking results as follows:
-[![car tracking result data](./assets/tracking_car_mask_1.jpg)](./assets/tracking_car_output.mp4)
+[![car tracking result data](./assets/tracking_car_mask_1.jpg)](https://github.com/user-attachments/assets/141594a2-1451-4d2e-a91b-7941284c2c13)
 
 
 ### Citation
diff --git a/assets/tracking_car_output.mp4 b/assets/tracking_car_output.mp4
deleted file mode 100644
index 05aa993..0000000
Binary files a/assets/tracking_car_output.mp4 and /dev/null differ
diff --git a/assets/zebra_output.mp4 b/assets/zebra_output.mp4
deleted file mode 100644
index e980dd4..0000000
Binary files a/assets/zebra_output.mp4 and /dev/null differ
diff --git a/grounded_sam2_tracking_demo_with_continuous_id.py b/grounded_sam2_tracking_demo_with_continuous_id.py
index 3568ae3..f620c28 100644
--- a/grounded_sam2_tracking_demo_with_continuous_id.py
+++ b/grounded_sam2_tracking_demo_with_continuous_id.py
@@ -68,7 +68,7 @@ frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
 
 # init video predictor state
 inference_state = video_predictor.init_state(video_path=video_dir)
-step = 10 # the step to sample frames for groundedDino predictor
+step = 25 # the step to sample frames for Grounding DINO predictor
 
 sam2_masks = MaskDictionatyModel()
 PROMPT_TYPE_FOR_VIDEO = "mask" # box, mask or point
@@ -195,4 +195,4 @@ Step 6: Draw the results and save the video
 """
 CommonUtils.draw_masks_and_box(video_dir, mask_data_dir, json_data_dir, result_dir)
 
-create_video_from_images(result_dir, output_video_path, frame_rate=15)
\ No newline at end of file
+create_video_from_images(result_dir, output_video_path, frame_rate=30)
\ No newline at end of file