0xVortex/RGB-T-Tracking-Papers-and-Results

A selection of RGB-T object tracking papers and their performance on various benchmarks.

28
/ 100
Experimental

This resource helps researchers and engineers evaluate and compare different methods for tracking objects using both visible light (RGB) and thermal infrared (TIR) cameras. It provides a concise overview of various object tracking algorithms, their performance metrics on standard datasets, and direct links to their corresponding research papers and code implementations. This is for anyone working on computer vision tasks that require robust object tracking in diverse environments, often involving scenarios where traditional RGB tracking falls short.

No commits in the last 6 months.

Use this if you need to quickly find the best-performing RGB-TIR object tracking algorithms for your specific application or research, and want direct access to their papers and code.

Not ideal if you are looking for a pre-built software tool or an in-depth tutorial on how to implement these algorithms yourself.

object-tracking thermal-imaging computer-vision multi-spectral-analysis autonomous-systems
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 16 / 25
Community 8 / 25

How are scores calculated?

Stars

8

Forks

1

Language

License

Apache-2.0

Last pushed

Aug 26, 2024

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/0xVortex/RGB-T-Tracking-Papers-and-Results"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.