# foolwood/benchmark_results

Visual Tracking Paper List

Repository: https://github.com/foolwood/benchmark_results
Canonical: https://ross.abutalabs.com/products/benchmark_results
License Family: other
Topics: paper, tracking, deep-learning, benchmark, visual-tracking
Last push: 2020-07-20T10:44:32+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3669, "days_push": 2235, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3944, forks 1025 (observed 2026-08-28T04:08:30.243906+00:00)

## What it is
A curated paper list for visual object tracking research, organized by venue and year, with links to papers and code. It serves as a reference catalog of tracking benchmarks and state-of-the-art trackers rather than runnable software.

## Use cases
- find papers on visual object tracking
- survey the state of the art in single object tracking
- find code implementations of tracking algorithms like DiMP or Siam R-CNN
- compare tracking benchmark results across papers
- start research in visual tracking

## When to choose
- you need a curated reading list of visual tracking papers with code links
- you are surveying tracking methods by conference and year
- you want quick pointers to benchmark trackers

## When to avoid
- you need runnable tracking software or a library
- you need papers after mid-2020, as the list is no longer updated
- you need a dataset itself rather than paper references

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, computer-vision
- domain: computer-vision, deep-learning, tutorials
- platform: cross-platform
- tags: awesome-list, paper-list, visual-tracking, object-tracking, benchmark, research

## Member repositories
- foolwood/benchmark_results (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:30.243906+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 2026-08-29T18:24:35.982133+00:00, confidence not recorded.
  - readme: https://github.com/foolwood/benchmark_results (fetched 2026-08-28T04:08:30.243906+00:00, sha e6db796cdc3a)
- Data as of 2026-08-30T08:39:29.467469+00:00.