trackforge 0.1.5

A unified, high-performance computer vision tracking library.

[!IMPORTANT] This project is currently under active development. APIs and features are subject to change.

Trackforge is a unified, high-performance computer vision tracking library, implemented in Rust and exposed as a Python package. It provides state-of-the-art tracking algorithms like ByteTrack, optimized for speed and ease of use in both Rust and Python environments.

Features

• 🚀 High Performance: Native Rust implementation for maximum speed and memory safety.
• 🐍 Python Bindings: Seamless integration with the Python ecosystem using pyo3.
• 🛠 Unified API: Consistent interface for tracking tasks across both languages.
• 📸 ByteTrack: Robust multi-object tracking using Kalman filters and IoU matching.

Roadmap

• DeepSORT: Integration with Re-ID models.
• SORT: Classic simple online and realtime tracking.
• BoT-SORT: Improvement over ByteTrack with camera motion compensation.

GPU Support & Architecture

Trackforge transforms detections into tracks. It is designed to be the high-speed CPU "glue" in your pipeline.

• Detectors (GPU): Your object detector (YOLOv8, Yolanas, etc.) runs on the GPU to produce bounding boxes.
• Trackforge (CPU): Receives these boxes and associates them on the CPU. Algorithms like ByteTrack are extremely efficient (less than 1ms per frame) and do not typically strictly require GPU acceleration, avoiding complex device transfers for the association step.
• Future: We may explore GPU-based association for massive-scale batch processing if data is already on-device.

Installation

Python

pip install trackforge

Rust

Add trackforge to your Cargo.toml:

[dependencies]
trackforge = "0.1.5" # Check crates.io for latest version

Usage

🐍 Python

import trackforge

# Initialize ByteTrack
# track_thresh: High confidence detection threshold (e.g., 0.5)
# track_buffer: Frames to keep lost tracks alive (e.g., 30)
# match_thresh: IoU matching threshold (e.g., 0.8)
# det_thresh: Initialization threshold (e.g., 0.6)
tracker = trackforge.ByteTrack(0.5, 30, 0.8, 0.6)

# Detections: List of (TLWH_Box, Score, ClassID)
detections = [
    ([100.0, 100.0, 50.0, 100.0], 0.9, 0),
    ([200.0, 200.0, 60.0, 120.0], 0.85, 0)
]

# Update tracker
tracks = tracker.update(detections)

for t in tracks:
    # t is (track_id, tlwh_box, score, class_id)
    print(f"ID: {t[0]}, Box: {t[1]}")

🦀 Rust

use trackforge::trackers::byte_track::ByteTrack;

fn main() -> anyhow::Result<()> {
    // Initialize ByteTrack
    let mut tracker = ByteTrack::new(0.5, 30, 0.8, 0.6);

    // Detections: Vec<([f32; 4], f32, i64)>
    let detections = vec![
        ([100.0, 100.0, 50.0, 100.0], 0.9, 0),
    ];

    // Update
    let tracks = tracker.update(detections);

    for t in tracks {
        println!("ID: {}, Box: {:?}", t.track_id, t.tlwh);
    }
    Ok(())
}

Development

This project uses maturin to manage the Rust/Python interop.

Prerequisites

• Rust & Cargo
• Python 3.8+
• maturin: pip install maturin

Build

# Build Python bindings
maturin develop

# Run Rust tests
cargo test

Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change.

License

MIT