ByteTrack Tracker

Overview

ByteTrack is a high-performance multi-object tracking algorithm used to track objects across video frames.

It works by associating detections from object detection models with existing tracks and assigning a unique tracker ID to each object.

ByteTrack is widely used in:

• Object tracking
• Surveillance systems
• Traffic monitoring
• Sports analytics
• Crowd analysis

The tracker works seamlessly with swatahVision Detections.

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Class: ByteTrack

class ByteTrack

The ByteTrack class performs multi-object tracking using bounding box detections.

It assigns persistent IDs to objects across frames.

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Constructor

ByteTrack(
    track_activation_threshold=0.25,
    lost_track_buffer=30,
    minimum_matching_threshold=0.8,
    frame_rate=30,
    minimum_consecutive_frames=1
)

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Parameters

Parameter	Type	Description
track_activation_threshold	float	Minimum detection confidence required to activate a track
lost_track_buffer	int	Number of frames a track is kept after it disappears
minimum_matching_threshold	float	IOU threshold used when matching tracks with detections
frame_rate	int	Frame rate of the processed video
minimum_consecutive_frames	int	Number of frames an object must appear before being considered a valid track

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How ByteTrack Works

ByteTrack tracks objects using the following pipeline:

1. Object Detection

Objects are detected using models such as:

• YOLO
• SSD

• RetinaNet

• Bounding Box Association

Detected boxes are matched with existing tracks using IoU distance.

1. Kalman Filter Prediction

Object motion is predicted using a Kalman Filter.

1. Track Management

Tracks are categorized as:

• Tracked – currently active objects
• Lost – temporarily missing objects
• Removed – expired tracks

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Method: update_with_detections

update_with_detections(detections: Detections) -> Detections

Updates the tracker using detection results and returns updated detections with tracker IDs.

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Parameters

Parameter	Type	Description
detections	Detections	Detection results containing bounding boxes and confidence scores

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Returns

Detections

Updated detections with tracker_id assigned.

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Example Usage

import swatahVision as sv
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
tracker = sv.ByteTrack()

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

def callback(frame, index):

    results = model(frame)[0]
    detections = sv.Detections.from_ultralytics(results)

    detections = tracker.update_with_detections(detections)

    labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]

    frame = box_annotator.annotate(
        scene=frame.copy(),
        detections=detections
    )

    frame = label_annotator.annotate(
        scene=frame,
        detections=detections,
        labels=labels
    )

    return frame

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Reset Tracker

tracker.reset()

Resets the internal state of the tracker.

This clears:

• tracked objects
• lost objects
• removed objects

Useful when processing multiple videos sequentially.

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Internal Tracking Pipeline

ByteTrack internally performs several steps:

1. Detection Filtering

Detections are separated based on confidence thresholds.

2. First Association

High confidence detections are matched with existing tracks.

3. Second Association

Lower confidence detections are matched to recover lost tracks.

4. Track Initialization

New tracks are created for unmatched detections.

5. Track Removal

Tracks that remain lost for too long are removed.

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Track States

Tracks can exist in three states:

State	Description
Tracked	Object is currently visible
Lost	Object temporarily disappeared
Removed	Object removed after long absence

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Helper Functions

ByteTrack uses several internal helper functions.

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joint_tracks

joint_tracks(track_list_a, track_list_b)

Combines two track lists while avoiding duplicate tracks.

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sub_tracks

sub_tracks(track_list_a, track_list_b)

Removes tracks from one list that exist in another list.

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remove_duplicate_tracks

remove_duplicate_tracks(tracks_a, tracks_b)

Removes duplicate tracks based on IoU similarity.

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Dependencies

ByteTrack relies on the following modules:

• NumPy
• Kalman Filter
• IoU matching utilities
• swatahVision Detection module

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Example Visualization

When combined with annotators like:

• BoxAnnotator
• LabelAnnotator
• TraceAnnotator

you can visualize object tracking across frames.

Example output:

Person #1
Person #2
Car #3

Each object keeps the same ID across frames.

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Summary

The ByteTrack tracker enables reliable multi-object tracking in video streams.

Key features:

• High accuracy tracking
• Kalman filter motion prediction
• Robust handling of occlusions
• Persistent object IDs
• Easy integration with detection models

It is an essential component for building real-time computer vision tracking systems.

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