RRepoGEO

REPOGEO REPORT · LITE

983632847/Awesome-Multimodal-Object-Tracking

Default branch main · commit 9651ebc1 · scanned 5/25/2026, 7:42:33 AM

GitHub: 1,052 stars · 57 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface 983632847/Awesome-Multimodal-Object-Tracking, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    awesome-list, multimodal-tracking, object-tracking, computer-vision, survey, research-papers, datasets
  • highreadme#2
    Clarify the repository's nature as an 'awesome list' in the README's opening

    Why:

    CURRENT
    > **A Comprehensive Survey: Awesome Multi-modal Object Tracking.** Chunhui Zhang, Li Liu, Hao Wen, Xi Zhou, Yanfeng Wang. [paper] [homepage][中文解读] > **<p align="justify"> Abstract:Multi-modal object tracking (MMOT) is an emerging field...
    COPY-PASTE FIX
    This repository is a curated "awesome list" tracking the latest progress in multi-modal object tracking, focusing on single-object tracking. It compiles research papers, datasets, and tools. It is based on the comprehensive survey: "Awesome Multi-modal Object Tracking" by Chunhui Zhang et al. [paper] [homepage][中文解读] <p align="justify"> Abstract:Multi-modal object tracking (MMOT) is an emerging field...
  • mediumhomepage#3
    Set the repository's homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/983632847/Awesome-Multimodal-Object-Tracking

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface 983632847/Awesome-Multimodal-Object-Tracking
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenCV
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenCV · recommended 2×
  2. ROS · recommended 2×
  3. PyTorch · recommended 1×
  4. TensorFlow · recommended 1×
  5. Kalman Filter · recommended 1×
  • CATEGORY QUERY
    How to track a single object using combined visual and thermal data?
    you: not recommended
    AI recommended (in order):
    1. OpenCV
    2. PyTorch
    3. TensorFlow
    4. Kalman Filter
    5. ROS
    6. MATLAB

    AI recommended 6 alternatives but never named 983632847/Awesome-Multimodal-Object-Tracking. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best techniques for robust object tracking across diverse sensor inputs?
    you: not recommended
    AI recommended (in order):
    1. filterpy
    2. OpenCV
    3. SORT
    4. DeepSORT
    5. ByteTrack
    6. GTSAM
    7. Ceres Solver
    8. ROS
    9. Apollo

    AI recommended 9 alternatives but never named 983632847/Awesome-Multimodal-Object-Tracking. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of 983632847/Awesome-Multimodal-Object-Tracking?
    pass
    AI did not name 983632847/Awesome-Multimodal-Object-Tracking — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts 983632847/Awesome-Multimodal-Object-Tracking in production, what risks or prerequisites should they evaluate first?
    pass
    AI named 983632847/Awesome-Multimodal-Object-Tracking explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo 983632847/Awesome-Multimodal-Object-Tracking solve, and who is the primary audience?
    pass
    AI did not name 983632847/Awesome-Multimodal-Object-Tracking — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of 983632847/Awesome-Multimodal-Object-Tracking. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite