Video-based Detection and Tracking with Improved Re-Identification Association for Pigs and Laying Hens in Farms

Qinghua Guo, Yue Sun, Lan Min, Arjen van Putten, Egbert Knol, Bram Visser, T. Rodenburg, J. Bolhuis, Piter Bijma, Peter H.N. de With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)

Abstract

It is important to detect negative behavior of animals for breeding in order to improve their health and welfare. In this work, AI is employed to assist individual animal detection and tracking, which enables the future analysis of behavior for individual animals. The study involves animal groups of pigs and laying hens. First, two state-of-the-art deep learning-based Multi-Object Tracking (MOT) methods are investigated, namely Joint Detection and Embedding (JDE) and FairMOT. Both models detect and track individual animals automatically and continuously. Second, a weighted association algorithm is proposed, which is feasible for both MOT methods to optimize the object re-identification (re-ID), thereby improving the tracking performance. The proposed methods are evaluated on manually annotated datasets. The best tracking performance on pigs is obtained by FairMOT with the weighted association, resulting in an IDF1 of 90.3%, MOTA of 90.8%, MOTP of 83.7%, number of identity switches of 14, and an execution rate of 20.48 fps. For the laying hens, FairMOT with the weighted association also achieves the best tracking performance, with an IDF1 of 88.8%, MOTA of 86.8%, MOTP of 72.8%, number of identity switches of 2, and an execution rate of 21.01 fps. These results show a promising high accuracy and robustness for the individual animal tracking.
Original languageEnglish
Title of host publicationProceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
EditorsGiovanni Maria Farinella, Petia Radeva, Kadi Bouatouch
PublisherSciTePress Digital Library
Pages69-78
Number of pages10
Volume4
ISBN (Electronic)978-989-758-555-5
DOIs
Publication statusPublished - 2022
Event17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2022 - Online
Duration: 6 Feb 20228 Feb 2022
Conference number: 17
https://visapp.scitevents.org/?y=2022

Conference

Conference17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2022
Abbreviated titleVISIGRAPP
Period6/02/228/02/22
Internet address

Keywords

  • Animal Detection
  • Animal Tracking
  • Multi-Object Tracking Models

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