Track Objects

Utilize Supervision to elevate your video analysis capabilities by effortlessly tracking objects identified by various object detection and segmentation models. This guide will walk you through the process of running inference using the 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] extension.

from supervision.assets import download_assets, VideoAssets

download_assets(VideoAssets.PEOPLE_WALKING)

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 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.

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
)

Tracking

After running inference and obtaining predictions, the next step is to track the detected objects throughout the video. Utilizing Supervision’s sv.ByteTrack functionality, each detected object is assigned a unique tracker ID, enabling the continuous following of the object's motion path across different frames.

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 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.

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
)

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, allows for visualizing the trajectories of objects, helping in understanding the movement patterns and interactions between objects in the video.

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
)

This structured walkthrough should give a detailed pathway to annotate videos effectively using Supervision’s various functionalities, including object tracking and trace annotations.