diff --git a/crabs/tracker/utils/io.py b/crabs/tracker/utils/io.py
index 16c82cda..36e85e8b 100644
--- a/crabs/tracker/utils/io.py
+++ b/crabs/tracker/utils/io.py
@@ -1,11 +1,13 @@
 """Utility functions for handling input and output operations."""
 
+import argparse
 import csv
 import os
 from datetime import datetime
 from pathlib import Path
 
 import cv2
+import matplotlib.pyplot as plt
 import numpy as np
 
 from crabs.detector.utils.visualization import draw_bbox
@@ -163,6 +165,7 @@ def save_required_output(
         frame_copy = frame.copy()
         for bbox in tracked_boxes:
             xmin, ymin, xmax, ymax, id = bbox
+
             draw_bbox(
                 frame_copy,
                 (xmin, ymin),
@@ -183,3 +186,153 @@ def release_video(video_output) -> None:
     """Release the video file if it's open."""
     if video_output:
         video_output.release()
+
+
+def read_metrics_from_csv(filename):
+    """Read the tracking output metrics from a CSV file.
+
+    To be called by plot_output_histogram.
+
+    Parameters
+    ----------
+    filename : str
+        Name of the CSV file to read.
+
+    Returns
+    -------
+    tuple:
+        Tuple containing lists of true positives, missed detections,
+        false positives, number of switches, and total ground truth
+        for each frame.
+
+    """
+    true_positives_list = []
+    missed_detections_list = []
+    false_positives_list = []
+    num_switches_list = []
+    total_ground_truth_list = []
+    mota_value_list = []
+
+    with open(filename) as file:
+        reader = csv.DictReader(file)
+        for row in reader:
+            true_positives_list.append(int(row["True Positives"]))
+            missed_detections_list.append(int(row["Missed Detections"]))
+            false_positives_list.append(int(row["False Positives"]))
+            num_switches_list.append(int(row["Number of Switches"]))
+            total_ground_truth_list.append(int(row["Total Ground Truth"]))
+            mota_value_list.append(float(row["Mota"]))
+
+    return (
+        true_positives_list,
+        missed_detections_list,
+        false_positives_list,
+        num_switches_list,
+        total_ground_truth_list,
+        mota_value_list,
+    )
+
+
+def plot_output_histogram(filename):
+    """Plot metrics along with the total ground truth for each frame.
+
+    Example usage:
+    > filename = <video_name>/tracking_metrics_output.csv
+    > python crabs/tracker/utils/io.py filename
+
+    Parameters
+    ----------
+    filename : str
+        Name of the CSV file to read.
+    true_positives_list : list[int]
+        List of counts of true positives for each frame.
+    missed_detections_list : list[int]
+        List of counts of missed detections for each frame.
+    false_positives_list : list[int]
+        List of counts of false positives for each frame.
+    num_switches_list : list[int]
+        List of counts of identity switches for each frame.
+    total_ground_truth_list : list[int]
+        List of total ground truth objects for each frame.
+
+    """
+    (
+        true_positives_list,
+        missed_detections_list,
+        false_positives_list,
+        num_switches_list,
+        total_ground_truth_list,
+        mota_value_list,
+    ) = read_metrics_from_csv(filename)
+    filepath = Path(filename)
+    plot_name = filepath.name
+
+    num_frames = len(true_positives_list)
+    frame_numbers = range(1, num_frames + 1)
+
+    plt.figure(figsize=(10, 6))
+
+    overall_mota = sum(mota_value_list) / len(mota_value_list)
+
+    # Calculate percentages
+    true_positives_percentage = [
+        tp / gt * 100 if gt > 0 else 0
+        for tp, gt in zip(true_positives_list, total_ground_truth_list)
+    ]
+    missed_detections_percentage = [
+        md / gt * 100 if gt > 0 else 0
+        for md, gt in zip(missed_detections_list, total_ground_truth_list)
+    ]
+    false_positives_percentage = [
+        fp / gt * 100 if gt > 0 else 0
+        for fp, gt in zip(false_positives_list, total_ground_truth_list)
+    ]
+    num_switches_percentage = [
+        ns / gt * 100 if gt > 0 else 0
+        for ns, gt in zip(num_switches_list, total_ground_truth_list)
+    ]
+
+    # Plot metrics
+    plt.plot(
+        frame_numbers,
+        true_positives_percentage,
+        label=f"True Positives ({sum(true_positives_list)})",
+        color="g",
+    )
+    plt.plot(
+        frame_numbers,
+        missed_detections_percentage,
+        label=f"Missed Detections ({sum(missed_detections_list)})",
+        color="r",
+    )
+    plt.plot(
+        frame_numbers,
+        false_positives_percentage,
+        label=f"False Positives ({sum(false_positives_list)})",
+        color="b",
+    )
+    plt.plot(
+        frame_numbers,
+        num_switches_percentage,
+        label=f"Number of Switches ({sum(num_switches_list)})",
+        color="y",
+    )
+
+    plt.xlabel("Frame Number")
+    plt.ylabel("Percentage of Total Ground Truth (%)")
+    plt.title(f"{plot_name}_mota:{overall_mota:.2f}")
+
+    plt.legend()
+    plt.savefig(f"{plot_name}.pdf")
+    plt.show()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Plot output histogram.")
+    parser.add_argument(
+        "filename",
+        type=str,
+        help="Path to the CSV file containing the metrics",
+    )
+    args = parser.parse_args()
+    plot_output_histogram(args.filename)