diff --git a/docs/en/guides/object-counting.md b/docs/en/guides/object-counting.md
index c00fbe19..0c3ce72a 100644
--- a/docs/en/guides/object-counting.md
+++ b/docs/en/guides/object-counting.md
@@ -36,7 +36,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
 !!! Example "Object Counting using YOLOv8 Example"
 
-    === "Region"
+    === "Count in Region"
 
         ```python
         from ultralytics import YOLO
@@ -78,8 +78,51 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
         video_writer.release()
         cv2.destroyAllWindows()
         ```
+    
+    === "Count in Polygon"
 
-    === "Line"
+        ```python
+        from ultralytics import YOLO
+        from ultralytics.solutions import object_counter
+        import cv2
+        
+        model = YOLO("yolov8n.pt")
+        cap = cv2.VideoCapture("path/to/video/file.mp4")
+        assert cap.isOpened(), "Error reading video file"
+        w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
+        
+        # Define region points as a polygon with 5 points
+        region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360), (20, 400)]
+        
+        # Video writer
+        video_writer = cv2.VideoWriter("object_counting_output.avi",
+                               cv2.VideoWriter_fourcc(*'mp4v'),
+                               fps,
+                               (w, h))
+        
+        # Init Object Counter
+        counter = object_counter.ObjectCounter()
+        counter.set_args(view_img=True,
+                         reg_pts=region_points,
+                         classes_names=model.names,
+                         draw_tracks=True)
+        
+        while cap.isOpened():
+            success, im0 = cap.read()
+            if not success:
+                print("Video frame is empty or video processing has been successfully completed.")
+                break
+            tracks = model.track(im0, persist=True, show=False)
+        
+            im0 = counter.start_counting(im0, tracks)
+            video_writer.write(im0)
+        
+        cap.release()
+        video_writer.release()
+        cv2.destroyAllWindows()
+        ```
+    
+    === "Count in Line"
 
         ```python
         from ultralytics import YOLO
diff --git a/docs/en/index.md b/docs/en/index.md
index cad30f3b..1c95078f 100644
--- a/docs/en/index.md
+++ b/docs/en/index.md
@@ -69,6 +69,7 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
 - [YOLOv6](https://github.com/meituan/YOLOv6) was open-sourced by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
 - [YOLOv7](https://github.com/WongKinYiu/yolov7) added additional tasks such as pose estimation on the COCO keypoints dataset.
 - [YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of YOLO by Ultralytics. As a cutting-edge, state-of-the-art (SOTA) model, YOLOv8 builds on the success of previous versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.
+- [YOLOv9] (https://github.com/WongKinYiu/yolov9) Introduces innovative methods like Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN).
 
 ## YOLO Licenses: How is Ultralytics YOLO licensed?