diff --git a/python/pyarmnn/examples/object_detection/run_video_file.py b/python/pyarmnn/examples/object_detection/run_video_file.py
index 4f06eb1..fc3e214 100644
--- a/python/pyarmnn/examples/object_detection/run_video_file.py
+++ b/python/pyarmnn/examples/object_detection/run_video_file.py
@@ -7,55 +7,19 @@
 """
 
 import os
+import sys
+script_dir = os.path.dirname(__file__)
+sys.path.insert(1, os.path.join(script_dir, '..', 'common'))
+
 import cv2
-import pyarmnn as ann
 from tqdm import tqdm
 from argparse import ArgumentParser
 
 from ssd import ssd_processing, ssd_resize_factor
 from yolo import yolo_processing, yolo_resize_factor
-from utils import create_video_writer, create_network, dict_labels, preprocess, execute_network, draw_bounding_boxes
-
-
-parser = ArgumentParser()
-parser.add_argument('--video_file_path', required=True, type=str,
-                    help='Path to the video file to run object detection on')
-parser.add_argument('--model_file_path', required=True, type=str,
-                    help='Path to the Object Detection model to use')
-parser.add_argument('--model_name', required=True, type=str,
-                    help='The name of the model being used. Accepted options: ssd_mobilenet_v1, yolo_v3_tiny')
-parser.add_argument('--label_path', type=str,
-                    help='Path to the labelset for the provided model file')
-parser.add_argument('--output_video_file_path', type=str,
-                    help='Path to the output video file with detections added in')
-parser.add_argument('--preferred_backends', type=str, nargs='+', default=['CpuAcc', 'CpuRef'],
-                    help='Takes the preferred backends in preference order, separated by whitespace, '
-                         'for example: CpuAcc GpuAcc CpuRef. Accepted options: [CpuAcc, CpuRef, GpuAcc]. '
-                         'Defaults to [CpuAcc, CpuRef]')
-args = parser.parse_args()
-
-
-def init_video(video_path: str, output_path: str):
-    """
-    Creates a video capture object from a video file.
-
-    Args:
-        video_path: User-specified video file path.
-        output_path: Optional path to save the processed video.
-
-    Returns:
-        Video capture object to capture frames, video writer object to write processed
-        frames to file, plus total frame count of video source to iterate through.
-    """
-    if not os.path.exists(video_path):
-        raise FileNotFoundError(f'Video file not found for: {video_path}')
-    video = cv2.VideoCapture(video_path)
-    if not video.isOpened:
-        raise RuntimeError(f'Failed to open video capture from file: {video_path}')
-
-    video_writer = create_video_writer(video, video_path, output_path)
-    iter_frame_count = range(int(video.get(cv2.CAP_PROP_FRAME_COUNT)))
-    return video, video_writer, iter_frame_count
+from utils import dict_labels
+from cv_utils import init_video_file_capture, preprocess, draw_bounding_boxes
+from network_executor import ArmnnNetworkExecutor
 
 
 def get_model_processing(model_name: str, video: cv2.VideoCapture, input_binding_info: tuple):
@@ -72,30 +36,29 @@
         Model labels, decoding and processing functions.
     """
     if model_name == 'ssd_mobilenet_v1':
-        labels = os.path.join('ssd_labels.txt')
+        labels = os.path.join(script_dir, 'ssd_labels.txt')
         return labels, ssd_processing, ssd_resize_factor(video)
     elif model_name == 'yolo_v3_tiny':
-        labels = os.path.join('yolo_labels.txt')
+        labels = os.path.join(script_dir, 'yolo_labels.txt')
         return labels, yolo_processing, yolo_resize_factor(video, input_binding_info)
     else:
         raise ValueError(f'{model_name} is not a valid model name')
 
 
 def main(args):
-    video, video_writer, frame_count = init_video(args.video_file_path, args.output_video_file_path)
-    net_id, runtime, input_binding_info, output_binding_info = create_network(args.model_file_path,
-                                                                              args.preferred_backends)
-    output_tensors = ann.make_output_tensors(output_binding_info)
-    labels, process_output, resize_factor = get_model_processing(args.model_name, video, input_binding_info)
-    labels = dict_labels(labels if args.label_path is None else args.label_path)
+    video, video_writer, frame_count = init_video_file_capture(args.video_file_path, args.output_video_file_path)
+
+    executor = ArmnnNetworkExecutor(args.model_file_path, args.preferred_backends)
+    labels, process_output, resize_factor = get_model_processing(args.model_name, video, executor.input_binding_info)
+    labels = dict_labels(labels if args.label_path is None else args.label_path, include_rgb=True)
 
     for _ in tqdm(frame_count, desc='Processing frames'):
         frame_present, frame = video.read()
         if not frame_present:
             continue
-        input_tensors = preprocess(frame, input_binding_info)
-        inference_output = execute_network(input_tensors, output_tensors, runtime, net_id)
-        detections = process_output(inference_output)
+        input_tensors = preprocess(frame, executor.input_binding_info)
+        output_result = executor.run(input_tensors)
+        detections = process_output(output_result)
         draw_bounding_boxes(frame, detections, resize_factor, labels)
         video_writer.write(frame)
     print('Finished processing frames')
@@ -103,4 +66,20 @@
 
 
 if __name__ == '__main__':
+    parser = ArgumentParser()
+    parser.add_argument('--video_file_path', required=True, type=str,
+                        help='Path to the video file to run object detection on')
+    parser.add_argument('--model_file_path', required=True, type=str,
+                        help='Path to the Object Detection model to use')
+    parser.add_argument('--model_name', required=True, type=str,
+                        help='The name of the model being used. Accepted options: ssd_mobilenet_v1, yolo_v3_tiny')
+    parser.add_argument('--label_path', type=str,
+                        help='Path to the labelset for the provided model file')
+    parser.add_argument('--output_video_file_path', type=str,
+                        help='Path to the output video file with detections added in')
+    parser.add_argument('--preferred_backends', type=str, nargs='+', default=['CpuAcc', 'CpuRef'],
+                        help='Takes the preferred backends in preference order, separated by whitespace, '
+                             'for example: CpuAcc GpuAcc CpuRef. Accepted options: [CpuAcc, CpuRef, GpuAcc]. '
+                             'Defaults to [CpuAcc, CpuRef]')
+    args = parser.parse_args()
     main(args)