diff --git a/people_tracking_v2/scripts/yolo_seg.py b/people_tracking_v2/scripts/yolo_seg.py
index 155b164..40c5d0a 100755
--- a/people_tracking_v2/scripts/yolo_seg.py
+++ b/people_tracking_v2/scripts/yolo_seg.py
@@ -7,6 +7,7 @@
 from sensor_msgs.msg import Image
 from people_tracking_v2.msg import Detection, DetectionArray, SegmentedImages
 from cv_bridge import CvBridge, CvBridgeError
+from kalman_filter import KalmanFilterCV  # Import the Kalman Filter class
 
 class YoloSegNode:
     def __init__(self):
@@ -16,6 +17,10 @@ def __init__(self):
         self.image_sub = rospy.Subscriber("/Webcam/image_raw", Image, self.image_callback)
         self.segmented_images_pub = rospy.Publisher("/segmented_images", SegmentedImages, queue_size=10)
         self.bounding_box_image_pub = rospy.Publisher("/bounding_box_image", Image, queue_size=10)
+        self.detection_pub = rospy.Publisher("/hero/predicted_detections", DetectionArray, queue_size=10)
+
+        # Initialize the Kalman Filter
+        self.kalman_filters = {}
 
     def image_callback(self, data):
         try:
@@ -60,6 +65,24 @@ def image_callback(self, data):
 
             # Draw bounding boxes and labels on the bounding_box_image
             x1, y1, x2, y2 = map(int, box)
+            x_center = (x1 + x2) / 2
+            y_center = (y1 + y2) / 2
+
+            # Initialize or update the Kalman Filter for this detection
+            if i not in self.kalman_filters:
+                self.kalman_filters[i] = KalmanFilterCV()
+            kalman_filter = self.kalman_filters[i]
+
+            # Update the Kalman Filter with the new measurement
+            kalman_filter.update(np.array([[x_center], [y_center]]))
+
+            # Predict the next position
+            kalman_filter.predict()
+            x_pred, y_pred = kalman_filter.get_state()[:2]
+
+            # Draw predicted position
+            cv2.circle(bounding_box_image, (int(x_pred), int(y_pred)), 5, (255, 0, 0), -1)
+
             color = (0, 255, 0)  # Set color for bounding boxes
             thickness = 3
             label_text = f'#{i+1} {int(label)}: {score:.2f}'
@@ -90,6 +113,9 @@ def image_callback(self, data):
         except CvBridgeError as e:
             rospy.logerr(e)
 
+        # Publish predicted detections
+        self.detection_pub.publish(detection_array)
+
 def main():
     rospy.init_node('yolo_seg_node', anonymous=True)
     yolo_node = YoloSegNode()
diff --git a/people_tracking_v2/src/people_tracking/kalman_filter.py b/people_tracking_v2/src/people_tracking/kalman_filter.py
new file mode 100644
index 0000000..5fe1d80
--- /dev/null
+++ b/people_tracking_v2/src/people_tracking/kalman_filter.py
@@ -0,0 +1,34 @@
+import numpy as np
+
+class KalmanFilterCV:
+    def __init__(self, dt=0.1):
+        # Kalman Filter parameters
+        self.dt = dt  # Time step
+        self.A = np.array([[1, 0, self.dt, 0], 
+                           [0, 1, 0, self.dt], 
+                           [0, 0, 1, 0], 
+                           [0, 0, 0, 1]])  # State transition matrix
+        self.H = np.array([[1, 0, 0, 0], 
+                           [0, 1, 0, 0]])  # Observation matrix
+        self.P = np.eye(4)  # Initial covariance matrix
+        self.Q = np.eye(4) * 0.01  # Process noise covariance
+        self.R = np.eye(2) * 0.1  # Measurement noise covariance
+        self.x = np.zeros((4, 1))  # Initial state [x, y, vx, vy]
+        self.P = np.eye(4)  # Initial covariance matrix
+
+    def predict(self):
+        # Predict the next state
+        self.x = np.dot(self.A, self.x)
+        self.P = np.dot(np.dot(self.A, self.P), self.A.T) + self.Q
+
+    def update(self, z):
+        # Update the state with the measurement
+        y = z - np.dot(self.H, self.x)  # Measurement residual
+        S = np.dot(self.H, np.dot(self.P, self.H.T)) + self.R  # Residual covariance
+        K = np.dot(np.dot(self.P, self.H.T), np.linalg.inv(S))  # Kalman gain
+        self.x = self.x + np.dot(K, y)
+        self.P = self.P - np.dot(np.dot(K, self.H), self.P)
+
+    def get_state(self):
+        # Return the current state
+        return self.x.flatten().tolist()