PigTracking: A benchmark dataset for pig tracking in videos
Abstract
The PigTracking dataset was designed to support the benchmarking of pig tracking algorithms from videos as introduced in the "A Large-Scale Longitudinal Dataset for Pig Tracking and Re-Identification" article to to evaluate the temporal consistency and performance of multi-object tracking (MOT) algorithms. Each sequence is organised as a single directory containing the RGB frames and all associated annotation files. The key files are gt.txt (full‑day MOT annotations), gt_filtered.txt (annotations for the frames in the sequence), annotations.json (COCO‑style metadata), and mot_labels.txt (additional instance‑level information). Pixel‑level masks and bounding box arrays are stored in the segmented/ folder, and baseline tracker outputs are provided as .txt files. The MOT ground truth files follow the standard MOT format: , , , , , , , , , , where the first six fields define the frame index, pig identity, and bounding box geometry. The data was acquired at three temporal resolutions, namely, 1, 2, and 5 frames-per-second (FPS), with the exact frame rate for each sequence indicated within its respective seqinfo.ini file. The data was captured over a two year period using 22 groups of pigs totalling 216 days of acquisition resulting in a dataset of over 740,000 labelled frames. Each data group of pigs was captured over 5.31 ± 1.97 weeks, featuring 7.62 ± 1.87 pigs per frame. Pigs entered the study at 6 weeks of age, and were recorded 5 - 19 times (mean ± s.d. = 10.3 ± 4.5) over 4 - 50 days (mean ± s.d. = 37.2 ± 14.1). The mean length of each imaging day was 47.34 ± 42.86 minutes. To benchmark the MOT algorithms, ten 5-minute-long videos were selected for each frame rate condition (1, 2, and 5 FPS) to form representative test groups. For the purposes of the article review, the selected 5-minute-long videos are included within this dataset to demonstrate the dataset's complexity and annotation quality. This dataset was extended as part of a companion research paper and is available at: https://doi.org/10.5281/zenodo.20415926. The full original dataset is hosted by UWE Bristol Research Data Repository: https://researchdata.uwe.ac.uk/id/eprint/791/. Additional components of the broader dataset ecosystem include: PigDetect — https://zenodo.org/records/18155376 PigTracking — https://zenodo.org/records/18155637 PigReID Part 1 — https://zenodo.org/records/18224572 PigReID Part 2 — https://zenodo.org/records/18224572 These related datasets provide complementary resources for pig detection, tracking, and ReID tasks and form the wider context in which this dataset extension was developed.
Fields of Study & Subjects
Video Surveillance and Tracking Methods
Smart Agriculture and AI
Autonomous Vehicle Technology and Safety
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 5 January 2026
- Language
- EN
- Document type
- Dataset