{
  "abstract": "Quantitative measurements of insect flight behaviour are essential for understanding the biomechanics, control, and ecology of flight, yet obtaining such measurements under free-flight conditions remains challenging. Small body size, rapid wing motion, visual symmetry, and frequent occlusions complicate three-dimensional pose estimation, often requiring restrictive experimental setups or substantial manual annotation. We present FLiTrak3D, an open-source Python package for estimating insect flight kinematics from multi-view videography. It combines machine-learning-based markerless tracking with biomechanical modelling to reconstruct and parametrise insect body and wing motion. The workflow integrates image preprocessing, including dynamic image cropping and enhancement, two-dimensional bodypart localisation, three-dimensional reconstruction, and optimisation-based skeletal fitting. A key innovation is the use of spatio-temporal encoding across synchronised camera views and adjacent frames to improve neural-network awareness of spatial and temporal context during bodypart localisation. Multi-view image stitching allows the network to use cross-view spatial relationships, reducing left-right bodypart misidentifications, while temporal encoding stacks consecutive greyscale frames into RGB images provide short-term motion information. A species-specific skeleton is then fitted to reconstructed keypoints to enforce kinematic constraints, and estimate body and wing orientations. We demonstrate the approach using free-flying Aedes aegypti mosquitoes, achieving bodypart localisation errors close to human labelling, and realistic flight kinematics.",
  "abstract_shown": true,
  "affiliations": [
    "Wagningen University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "A. Cribellier"
    },
    {
      "name": "A.-J. Buchner"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/42h6mh9ectg6v.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.07.749842",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749842v1.full",
  "id": "42h6mh9ectg6v",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749842v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "zoology"
    ],
    [
      "openalex",
      "1707|Human Pose and Action Recognition"
    ]
  ],
  "openalex": "W7212229025",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749842v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/42h6mh9ectg6v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:04Z",
      "native_id": "10.64898/2026.09.07.749842",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.07.749842v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:zoology",
      "subfield": "zoology-behaviour"
    }
  ],
  "title": "FLiTrak3D: Improved deep-learning-based 3D insect flightkinematics tracking using spatial and temporal encoding",
  "title_text": "FLiTrak3D: Improved deep-learning-based 3D insect flightkinematics tracking using spatial and temporal encoding",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:04Z",
  "url": "https://research.draconitic.com/paper/42h6mh9ectg6v",
  "venue": "bioRxiv",
  "versions": []
}