diff --git a/docs/how_to/track_objects.md b/docs/how_to/track_objects.md
index c9497945..ea48b070 100644
--- a/docs/how_to/track_objects.md
+++ b/docs/how_to/track_objects.md
@@ -1 +1,183 @@
-🚧 Page under construction.
+Utilize Supervision to elevate your video analysis capabilities by effortlessly
+[tracking](https://supervision.roboflow.com/trackers/) objects identified by various
+object detection and segmentation models. This guide will walk you through the process
+of running inference using the [Ultralytics](https://github.com/ultralytics/ultralytics)
+YOLOv8 model, subsequently tracking these objects, and annotating the video.
+
+To make it easier for you to follow our tutorial download the video we will use as an
+example. You can do this using
+[`supervision[assets]`](https://supervision.roboflow.com/assets/) extension.
+
+```python
+from supervision.assets import download_assets, VideoAssets
+
+download_assets(VideoAssets.PEOPLE_WALKING)
+```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/people-walking.mp4" type="video/mp4">
+</video>
+
+## Run Inference
+
+First, you'll need to obtain predictions from your object detection or segmentation
+model. In this tutorial, we are using the YOLOv8 model as an example. However,
+Supervision is versatile and compatible with various models. Check this
+[link](https://supervision.roboflow.com/how_to/detect_and_annotate/#load-predictions-into-supervision)
+for guidance on how to plug in other models.
+
+We will define a `callback` function, which will process each frame of the video
+by obtaining model predictions and then annotating the frame based on these predictions.
+This `callback` function will be essential in the subsequent steps of the tutorial, as
+it will be modified to include tracking, labeling, and trace annotations.
+
+```{ .py }
+import numpy as np
+import supervision as sv
+from ultralytics import YOLO
+
+model = YOLO("yolov8n.pt")
+box_annotator = sv.BoundingBoxAnnotator()
+
+def callback(frame: np.ndarray, _: int) -> np.ndarray:
+    results = model(frame)[0]
+    detections = sv.Detections.from_ultralytics(results)
+    return box_annotator.annotate(frame.copy(), detections=detections)
+
+sv.process_video(
+    source_path="people-walking.mp4",
+    target_path="result.mp4",
+    callback=callback
+)
+```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/run-inference.mp4" type="video/mp4">
+</video>
+
+## Tracking
+
+After running inference and obtaining predictions, the next step is to track the
+detected objects throughout the video. Utilizing Supervision’s
+[`sv.ByteTrack`](https://supervision.roboflow.com/trackers/#supervision.tracker.byte_tracker.core.ByteTrack)
+functionality, each detected object is assigned a unique tracker ID,
+enabling the continuous following of the object's motion path across different frames.
+
+```{ .py hl_lines="6 12" }
+import numpy as np
+import supervision as sv
+from ultralytics import YOLO
+
+model = YOLO("yolov8n.pt")
+tracker = sv.ByteTrack()
+box_annotator = sv.BoundingBoxAnnotator()
+
+def callback(frame: np.ndarray, _: int) -> np.ndarray:
+    results = model(frame)[0]
+    detections = sv.Detections.from_ultralytics(results)
+    detections = tracker.update_with_detections(detections)
+    return box_annotator.annotate(frame.copy(), detections=detections)
+
+sv.process_video(
+    source_path="people-walking.mp4",
+    target_path="result.mp4",
+    callback=callback
+)
+```
+
+## Annotate Video with Tracking IDs
+
+Annotating the video with tracking IDs helps in distinguishing and following each object
+distinctly. With the
+[`sv.LabelAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.LabelAnnotator)
+in Supervision, we can overlay the tracker IDs and class labels on the detected objects,
+offering a clear visual representation of each object's class and unique identifier.
+
+```{ .py hl_lines="8 15-19 23-24" }
+import numpy as np
+import supervision as sv
+from ultralytics import YOLO
+
+model = YOLO("yolov8n.pt")
+tracker = sv.ByteTrack()
+box_annotator = sv.BoundingBoxAnnotator()
+label_annotator = sv.LabelAnnotator()
+
+def callback(frame: np.ndarray, _: int) -> np.ndarray:
+    results = model(frame)[0]
+    detections = sv.Detections.from_ultralytics(results)
+    detections = tracker.update_with_detections(detections)
+
+    labels = [
+        f"#{tracker_id} {results.names[class_id]}"
+        for class_id, tracker_id
+        in zip(detections.class_id, detections.tracker_id)
+    ]
+
+    annotated_frame = box_annotator.annotate(
+        frame.copy(), detections=detections)
+    return label_annotator.annotate(
+        annotated_frame, detections=detections, labels=labels)
+
+sv.process_video(
+    source_path="people-walking.mp4",
+    target_path="result.mp4",
+    callback=callback
+)
+```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/annotate-video-with-tracking-ids.mp4" type="video/mp4">
+</video>
+
+## Annotate Video with Traces
+
+Adding traces to the video involves overlaying the historical paths of the detected
+objects. This feature, powered by the
+[`sv.TraceAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.TraceAnnotator),
+allows for visualizing the trajectories of objects, helping in understanding the
+movement patterns and interactions between objects in the video.
+
+```{ .py hl_lines="9 26-27" }
+import numpy as np
+import supervision as sv
+from ultralytics import YOLO
+
+model = YOLO("yolov8n.pt")
+tracker = sv.ByteTrack()
+box_annotator = sv.BoundingBoxAnnotator()
+label_annotator = sv.LabelAnnotator()
+trace_annotator = sv.TraceAnnotator()
+
+def callback(frame: np.ndarray, _: int) -> np.ndarray:
+    results = model(frame)[0]
+    detections = sv.Detections.from_ultralytics(results)
+    detections = tracker.update_with_detections(detections)
+
+    labels = [
+        f"#{tracker_id} {results.names[class_id]}"
+        for class_id, tracker_id
+        in zip(detections.class_id, detections.tracker_id)
+    ]
+
+    annotated_frame = box_annotator.annotate(
+        frame.copy(), detections=detections)
+    annotated_frame = label_annotator.annotate(
+        annotated_frame, detections=detections, labels=labels)
+    return trace_annotator.annotate(
+        annotated_frame, detections=detections)
+
+sv.process_video(
+    source_path="people-walking.mp4",
+    target_path="result.mp4",
+    callback=callback
+)
+```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/annotate-video-with-traces.mp4" type="video/mp4">
+</video>
+
+This structured walkthrough should give a detailed pathway to annotate videos
+effectively using Supervision’s various functionalities, including object tracking and
+trace annotations.
diff --git a/supervision/annotators/core.py b/supervision/annotators/core.py
index 3fbd7a85..95cea69e 100644
--- a/supervision/annotators/core.py
+++ b/supervision/annotators/core.py
@@ -882,7 +882,7 @@ class BlurAnnotator(BaseAnnotator):
             >>> detections = sv.Detections(...)
 
             >>> blur_annotator = sv.BlurAnnotator()
-            >>> annotated_frame = blur_annotator.annotate(
+            >>> annotated_frame = circle_annotator.annotate(
             ...     scene=image.copy(),
             ...     detections=detections
             ... )