# 983632847/Awesome-Multimodal-Object-Tracking

A continuously updated project to track the latest progress in the field of multi-modal object tracking. This project focuses solely on single-object tracking.

Repository: https://github.com/983632847/Awesome-Multimodal-Object-Tracking
Canonical: https://ross.abutalabs.com/products/awesome-multimodal-object-tracking
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-09-02T01:20:36+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 100, release rhythm 35, longevity 59
- inputs: {"age_days": 836, "days_push": 1, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1057, forks 56 (observed 2026-09-03T02:15:00.448709+00:00)

## What it is
A continuously updated awesome-list and survey companion repository for multi-modal object tracking (MMOT), covering combinations of RGB, depth, thermal infrared, event, language, and audio modalities. It catalogs papers, datasets, and algorithms organized by task category and technical paradigm.

## Use cases
- find recent papers on multi-modal object tracking
- survey RGB+thermal infrared tracking methods
- discover multi-modal tracking benchmark datasets
- research prompt learning or state space models for tracking
- start literature review for a tracking thesis
- compare tracking algorithms across modalities

## When to choose
- you need a curated, continuously updated bibliography of MMOT research
- you want an overview of datasets and algorithm paradigms across tracking modalities
- you are surveying single-object tracking with multiple sensor modalities

## When to avoid
- you need runnable tracking code or a library rather than a paper list
- you are working on multi-object tracking, which is out of scope
- you need a maintained software tool rather than a learning resource

## Facets
- artifact type: learning-resource
- maturity: active
- function: computer-vision, video-processing, documentation
- domain: computer-vision, artificial-intelligence, tutorials
- platform: cross-platform
- tags: awesome-list, multi-modal-tracking, object-tracking, survey, research-paper-list, rgb-t-tracking, rgb-d-tracking, rgb-e-tracking, rgb-l-tracking, single-object-tracking

## Member repositories
- 983632847/Awesome-Multimodal-Object-Tracking (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:00.448709+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:58:02.710870+00:00, confidence not recorded.
  - readme: https://github.com/983632847/Awesome-Multimodal-Object-Tracking (fetched 2026-09-03T02:15:00.448709+00:00, sha 0cd7ddf3690f)
- Data as of 2026-08-30T08:39:29.467469+00:00.