diff --git a/people_tracking_v2/CMakeLists.txt b/people_tracking_v2/CMakeLists.txt
index 086bbf7..660aefd 100644
--- a/people_tracking_v2/CMakeLists.txt
+++ b/people_tracking_v2/CMakeLists.txt
@@ -37,6 +37,7 @@ install(PROGRAMS
   scripts/face_recognition_node.py
   scripts/pose_estimation_node.py
   scripts/yolo.py
+  scripts/yolo_seg.py
   scripts/HoC.py
   DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
 )
diff --git a/people_tracking_v2/scripts/yolo_seg.py b/people_tracking_v2/scripts/yolo_seg.py
new file mode 100755
index 0000000..52bd275
--- /dev/null
+++ b/people_tracking_v2/scripts/yolo_seg.py
@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+
+import rospy
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from sensor_msgs.msg import Image
+from people_tracking_v2.msg import Detection, DetectionArray
+from cv_bridge import CvBridge, CvBridgeError
+
+class YoloSegNode:
+    def __init__(self):
+        self.bridge = CvBridge()
+        self.model = YOLO("yolov8n-seg.pt")  # Ensure the model supports segmentation
+
+        self.image_sub = rospy.Subscriber("/Webcam/image_raw", Image, self.image_callback)
+        self.segmented_image_pub = rospy.Publisher("/segmented_image", Image, queue_size=10)
+        self.bounding_box_image_pub = rospy.Publisher("/bounding_box_image", Image, queue_size=10)
+
+    def image_callback(self, data):
+        try:
+            cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
+        except CvBridgeError as e:
+            rospy.logerr(e)
+            return
+
+        # Run the YOLOv8 model on the frame
+        results = self.model(cv_image)[0]
+
+        # Extract the detections from the result
+        boxes = results.boxes.xyxy.cpu().numpy()
+        scores = results.boxes.conf.cpu().numpy()
+        labels = results.boxes.cls.cpu().numpy()
+        masks = results.masks.data.cpu().numpy() if results.masks else None
+
+        rospy.loginfo(f"Total Detections: {len(labels)}")  # Log the total number of detections
+
+        detection_array = DetectionArray()
+
+        # Create a copy of the image for bounding box visualization
+        bounding_box_image = cv_image.copy()
+
+        # Create a blank mask for accumulating all segmentation masks
+        combined_mask = np.zeros(cv_image.shape[:2], dtype=np.uint8)
+
+        # Process each detection and create a Detection message, but only for humans (class 0)
+        human_detections = [(box, score, label, mask) for box, score, label, mask in zip(boxes, scores, labels, masks) if int(label) == 0]
+        rospy.loginfo(f"Human Detections: {len(human_detections)}")  # Log the number of human detections
+
+        for i, (box, score, label, mask) in enumerate(human_detections):
+            detection = Detection()
+            detection.x1 = float(box[0])
+            detection.y1 = float(box[1])
+            detection.x2 = float(box[2])
+            detection.y2 = float(box[3])
+            detection.score = float(score)
+            detection.label = int(label)
+            detection_array.detections.append(detection)
+
+            # Draw bounding boxes and labels on the bounding_box_image
+            x1, y1, x2, y2 = map(int, box)
+            color = (0, 255, 0)  # Set color for bounding boxes
+            thickness = 3
+            label_text = f'#{i+1} {int(label)}: {score:.2f}'
+            cv2.rectangle(bounding_box_image, (x1, y1), (x2, y2), color, thickness)
+            cv2.putText(
+                bounding_box_image, label_text, (x1, y1 - 10),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1
+            )
+
+            # Apply segmentation mask to the combined_mask
+            if mask is not None:
+                # Resize mask to match the original image dimensions
+                resized_mask = cv2.resize(mask, (cv_image.shape[1], cv_image.shape[0]))
+                resized_mask = resized_mask.astype(np.uint8)
+
+                # Accumulate the mask
+                combined_mask = cv2.bitwise_or(combined_mask, resized_mask)
+
+        # Apply the combined mask to the original image
+        segmented_image = cv2.bitwise_and(cv_image, cv_image, mask=combined_mask)
+
+        # Publish segmented image
+        try:
+            segmented_image_msg = self.bridge.cv2_to_imgmsg(segmented_image, "bgr8")
+            self.segmented_image_pub.publish(segmented_image_msg)
+        except CvBridgeError as e:
+            rospy.logerr(e)
+
+        # Publish bounding box image
+        try:
+            bounding_box_image_msg = self.bridge.cv2_to_imgmsg(bounding_box_image, "bgr8")
+            self.bounding_box_image_pub.publish(bounding_box_image_msg)
+        except CvBridgeError as e:
+            rospy.logerr(e)
+
+def main():
+    rospy.init_node('yolo_node', anonymous=True)
+    yolo_node = YoloSegNode()
+    try:
+        rospy.spin()
+    except KeyboardInterrupt:
+        print("Shutting down")
+    cv2.destroyAllWindows()
+
+if __name__ == '__main__':
+    main()
diff --git a/people_tracking_v2/src/people_tracking/launchers/yolo_launcher.launch b/people_tracking_v2/src/people_tracking/launchers/yolo_launcher.launch
index 0744913..b841cd0 100644
--- a/people_tracking_v2/src/people_tracking/launchers/yolo_launcher.launch
+++ b/people_tracking_v2/src/people_tracking/launchers/yolo_launcher.launch
@@ -25,6 +25,13 @@
     output="screen"
   />
 
+  <node
+    pkg="people_tracking_v2"
+    type="yolo_seg.py"
+    name="Image_seg"
+    output="screen"
+  />
+
   <node
     pkg="people_tracking_v2"
     type="HoC.py"