{
  "abstract": "Individual encounter histories are central to capture-recapture models, but fisheries monitoring is often reduced to counts that do not account for variation in detection probability. This study presents a machine-vision workflow for converting long-term video surveillance of Atlantic salmon (Salmo salar) and sea trout (Salmo trutta) spawning runs into individual-level data for capture-recapture analysis. The workflow detects fish-positive frames, saves video clips, extracts fish-head regions of interest, and organizes cropped images for re-identification. A binary EfficientNetB0 fish detector achieved validation accuracy of 0.9918 and PR AUC of 0.9992; at a conservative threshold of 0.98, validation false positives were eliminated while retaining 94.3% recall. A YOLOv8n model localized fish-head regions, and an EfficientNetB0 ArcFace model trained on head images from 700 identities achieved 99.80% accuracy among accepted known matches and an image-weighted false-accept rate of 0.81% when non-training identities were treated as unknown. Zero-shot closed-set retrieval achieved 99.17% top-1 accuracy across 1,416 identities excluded from model training, demonstrating strong generalization to previously unseen identities.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Freshwater Research, Swedish University of Agricultural Sciences"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "K. Karlsson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c6gf6rpy554p7.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.03.749040",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749040v1.full",
  "id": "c6gf6rpy554p7",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749040v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "ecology"
    ],
    [
      "openalex",
      "2312|Water Quality Monitoring Technologies"
    ]
  ],
  "openalex": "W7208733160",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749040v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c6gf6rpy554p7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:30Z",
      "native_id": "10.64898/2026.09.03.749040",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749040v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:ecology",
      "subfield": "ecology"
    }
  ],
  "title": "From video to encounter histories: individual identification and machine vision for salmonid capture-recapture monitoring",
  "title_text": "From video to encounter histories: individual identification and machine vision for salmonid capture-recapture monitoring",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:30Z",
  "url": "https://research.draconitic.com/paper/c6gf6rpy554p7",
  "venue": "bioRxiv",
  "versions": []
}