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diff --git a/python/pyarmnn/examples/object_detection/utils.py b/python/pyarmnn/examples/object_detection/utils.py
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+# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# SPDX-License-Identifier: MIT
+
+"""
+This file contains shared functions used in the object detection scripts for
+preprocessing data, preparing the network and postprocessing.
+"""
+
+import os
+import cv2
+import numpy as np
+import pyarmnn as ann
+
+
+def create_video_writer(video: cv2.VideoCapture, video_path: str, output_path: str):
+    """
+    Creates a video writer object to write processed frames to file.
+
+    Args:
+        video: Video capture object, contains information about data source.
+        video_path: User-specified video file path.
+        output_path: Optional path to save the processed video.
+
+    Returns:
+        Video writer object.
+    """
+    _, ext = os.path.splitext(video_path)
+
+    if output_path is not None:
+        assert os.path.isdir(output_path)
+
+    i, filename = 0, os.path.join(output_path if output_path is not None else str(), f'object_detection_demo{ext}')
+    while os.path.exists(filename):
+        i += 1
+        filename = os.path.join(output_path if output_path is not None else str(), f'object_detection_demo({i}){ext}')
+
+    video_writer = cv2.VideoWriter(filename=filename,
+                                   fourcc=cv2.VideoWriter_fourcc(*'mp4v'),
+                                   fps=int(video.get(cv2.CAP_PROP_FPS)),
+                                   frameSize=(int(video.get(cv2.CAP_PROP_FRAME_WIDTH)),
+                                              int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))))
+    return video_writer
+
+
+def create_network(model_file: str, backends: list):
+    """
+    Creates a network based on the model file and a list of backends.
+
+    Args:
+        model_file: User-specified model file.
+        backends: List of backends to optimize network.
+
+    Returns:
+        net_id: Unique ID of the network to run.
+        runtime: Runtime context for executing inference.
+        input_binding_info: Contains essential information about the model input.
+        output_binding_info: Used to map output tensor and its memory.
+    """
+    if not os.path.exists(model_file):
+        raise FileNotFoundError(f'Model file not found for: {model_file}')
+
+    # Determine which parser to create based on model file extension
+    parser = None
+    _, ext = os.path.splitext(model_file)
+    if ext == '.tflite':
+        parser = ann.ITfLiteParser()
+    elif ext == '.pb':
+        parser = ann.ITfParser()
+    elif ext == '.onnx':
+        parser = ann.IOnnxParser()
+    assert (parser is not None)
+    network = parser.CreateNetworkFromBinaryFile(model_file)
+
+    # Specify backends to optimize network
+    preferred_backends = []
+    for b in backends:
+        preferred_backends.append(ann.BackendId(b))
+
+    # Select appropriate device context and optimize the network for that device
+    options = ann.CreationOptions()
+    runtime = ann.IRuntime(options)
+    opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(),
+                                  ann.OptimizerOptions())
+    print(f'Preferred backends: {backends}\n{runtime.GetDeviceSpec()}\n'
+          f'Optimization warnings: {messages}')
+
+    # Load the optimized network onto the Runtime device
+    net_id, _ = runtime.LoadNetwork(opt_network)
+
+    # Get input and output binding information
+    graph_id = parser.GetSubgraphCount() - 1
+    input_names = parser.GetSubgraphInputTensorNames(graph_id)
+    input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])
+    output_names = parser.GetSubgraphOutputTensorNames(graph_id)
+    output_binding_info = []
+    for output_name in output_names:
+        outBindInfo = parser.GetNetworkOutputBindingInfo(graph_id, output_name)
+        output_binding_info.append(outBindInfo)
+    return net_id, runtime, input_binding_info, output_binding_info
+
+
+def dict_labels(labels_file: str):
+    """
+    Creates a labels dictionary from the input labels file.
+
+    Args:
+        labels_file: Default or user-specified file containing the model output labels.
+
+    Returns:
+        A dictionary keyed on the classification index with values corresponding to
+        labels and randomly generated RGB colors.
+    """
+    labels_dict = {}
+    with open(labels_file, 'r') as labels:
+        for index, line in enumerate(labels, 0):
+            labels_dict[index] = line.strip('\n'), tuple(np.random.random(size=3) * 255)
+        return labels_dict
+
+
+def resize_with_aspect_ratio(frame: np.ndarray, input_binding_info: tuple):
+    """
+    Resizes frame while maintaining aspect ratio, padding any empty space.
+
+    Args:
+        frame: Captured frame.
+        input_binding_info: Contains shape of model input layer.
+
+    Returns:
+        Frame resized to the size of model input layer.
+    """
+    aspect_ratio = frame.shape[1] / frame.shape[0]
+    model_height, model_width = list(input_binding_info[1].GetShape())[1:3]
+
+    if aspect_ratio >= 1.0:
+        new_height, new_width = int(model_width / aspect_ratio), model_width
+        b_padding, r_padding = model_height - new_height, 0
+    else:
+        new_height, new_width = model_height, int(model_height * aspect_ratio)
+        b_padding, r_padding = 0, model_width - new_width
+
+    # Resize and pad any empty space
+    frame = cv2.resize(frame, (new_width, new_height), interpolation=cv2.INTER_LINEAR)
+    frame = cv2.copyMakeBorder(frame, top=0, bottom=b_padding, left=0, right=r_padding,
+                               borderType=cv2.BORDER_CONSTANT, value=[0, 0, 0])
+    return frame
+
+
+def preprocess(frame: np.ndarray, input_binding_info: tuple):
+    """
+    Takes a frame, resizes, swaps channels and converts data type to match
+    model input layer. The converted frame is wrapped in a const tensor
+    and bound to the input tensor.
+
+    Args:
+        frame: Captured frame from video.
+        input_binding_info:  Contains shape and data type of model input layer.
+
+    Returns:
+        Input tensor.
+    """
+    # Swap channels and resize frame to model resolution
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    resized_frame = resize_with_aspect_ratio(frame, input_binding_info)
+
+    # Expand dimensions and convert data type to match model input
+    data_type = np.float32 if input_binding_info[1].GetDataType() == ann.DataType_Float32 else np.uint8
+    resized_frame = np.expand_dims(np.asarray(resized_frame, dtype=data_type), axis=0)
+    assert resized_frame.shape == tuple(input_binding_info[1].GetShape())
+
+    input_tensors = ann.make_input_tensors([input_binding_info], [resized_frame])
+    return input_tensors
+
+
+def execute_network(input_tensors: list, output_tensors: list, runtime, net_id: int) -> np.ndarray:
+    """
+    Executes inference for the loaded network.
+
+    Args:
+        input_tensors: The input frame tensor.
+        output_tensors: The output tensor from output node.
+        runtime: Runtime context for executing inference.
+        net_id: Unique ID of the network to run.
+
+    Returns:
+        Inference results as a list of ndarrays.
+    """
+    runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
+    output = ann.workload_tensors_to_ndarray(output_tensors)
+    return output
+
+
+def draw_bounding_boxes(frame: np.ndarray, detections: list, resize_factor, labels: dict):
+    """
+    Draws bounding boxes around detected objects and adds a label and confidence score.
+
+    Args:
+        frame: The original captured frame from video source.
+        detections: A list of detected objects in the form [class, [box positions], confidence].
+        resize_factor: Resizing factor to scale box coordinates to output frame size.
+        labels: Dictionary of labels and colors keyed on the classification index.
+    """
+    for detection in detections:
+        class_idx, box, confidence = [d for d in detection]
+        label, color = labels[class_idx][0].capitalize(), labels[class_idx][1]
+
+        # Obtain frame size and resized bounding box positions
+        frame_height, frame_width = frame.shape[:2]
+        x_min, y_min, x_max, y_max = [int(position * resize_factor) for position in box]
+
+        # Ensure box stays within the frame
+        x_min, y_min = max(0, x_min), max(0, y_min)
+        x_max, y_max = min(frame_width, x_max), min(frame_height, y_max)
+
+        # Draw bounding box around detected object
+        cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, 2)
+
+        # Create label for detected object class
+        label = f'{label} {confidence * 100:.1f}%'
+        label_color = (0, 0, 0) if sum(color)>200 else (255, 255, 255)
+
+        # Make sure label always stays on-screen
+        x_text, y_text = cv2.getTextSize(label, cv2.FONT_HERSHEY_DUPLEX, 1, 1)[0][:2]
+
+        lbl_box_xy_min = (x_min, y_min if y_min<25 else y_min - y_text)
+        lbl_box_xy_max = (x_min + int(0.55 * x_text), y_min + y_text if y_min<25 else y_min)
+        lbl_text_pos = (x_min + 5, y_min + 16 if y_min<25 else y_min - 5)
+
+        # Add label and confidence value
+        cv2.rectangle(frame, lbl_box_xy_min, lbl_box_xy_max, color, -1)
+        cv2.putText(frame, label, lbl_text_pos, cv2.FONT_HERSHEY_DUPLEX, 0.50,
+                    label_color, 1, cv2.LINE_AA)