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Wizard: Unsupervised goats tracking algorithm

Wizard

64 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Computer vision is an interesting tool for animal behavior monitoring, mainly because it limits animal handling and it can be used to record various traits using only one sensor. From previous studies, this technic has shown to be suitable for various species and behavior. However it remains challenging to collect individual information, i.e. not only to detect animals and behavior on the video frames, but also to identify them. Animal identification is a prerequisite to gather individual information in order to characterize individuals and compare them. A common solution to this problem, known as multiple objects tracking, consists in detecting the animals on each video frame, and then associate detections to a unique animal ID. Association of detections between two consecutive frames are generally made to maintain coherence of the detection locations and appearances. To extract appearance information, a common solution is to use a convolutional neural network (CNN), trained on a large dataset before running the tracking algorithm. For farmed animals, designing such network is challenging as far as large training dataset are still lacking. In this article, we proposed an innovative solution, where the CNN used to extract appearance information is parameterized using offline unsupervised training. The algorithm, named Wizard, was evaluated for the purpose of goats monitoring in outdoor conditions. 17 annotated videos were used, for a total of 4H30, with various number of animals on the video (from 3 to 8) and different level of color differences between animals. First, the ability of the algorithm to track the detected animals was evaluated. When animals were detected, the algorithm found the correct animal ID in 94.82% of the frames. When tracking and detection were evaluated together, we found that Wizard found the correct animal ID in 86.18% of the video length. In situations where the animal detection rate could be high, Wizard seems to be a suitable solution for individual behavior analysis experiments based on computer vision.

Papers archive 2025-07-28

30 shown of 64, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 61 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Question Answering6
Dialogue Generation4
Response Generation4
BIG-bench Machine Learning3
Information Retrieval3
Retrieval3
Speech Recognition3
Conversational Question Answering2
Conversational Search2
Decision Making2
Depression Detection2
Language Modeling2
Language Modelling2
Transfer Learning2
speech-recognition2
AI Agent1
Articles1
Automatic Speech Recognition1
Automatic Speech Recognition (ASR)1
Benchmarking1

Usage over time archive 2025-07-28

Papers per year tagged with Wizard: 2012 to 2025, peak 9 9 0 2012: 1 paper 2012 2013: 0 papers 2013 2014: 3 papers 2014 2015: 0 papers 2015 2016: 6 papers 2016 2017: 2 papers 2017 2018: 1 paper 2018 2019: 4 papers 2019 2020: 9 papers 2020 2021: 5 papers 2021 2022: 9 papers 2022 2023: 9 papers 2023 2024: 9 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (64 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Multi-Object Tracking Models

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