Scholarlink

<p>Monitoring a group of animals would greatly benefit from automated animal re-identification from video. Multiple Object Tracking alone does not provide a sufficiently good re-identification, hence we propose to augment the process by further clustering the output tracks. Unlike datasets for person and vehicle identification, existing animal datasets are not substantial enough to train an advanced model for conventional clustering. In this paper, we present a Classification-Based Clustering method (CBC) which employs track labels and temporal constraints to train a bespoke model for each video dataset. Our proposed method works better than using the tracks alone as animal identities. It also outperforms 13 alternative clustering methods applied to the tracking results.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Animal re-identification in video through track clustering

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月19日

查看全文
  • Francis J. Williams,
  • Samuel L. Hennessey,
  • Ludmila I. Kuncheva

摘要

Monitoring a group of animals would greatly benefit from automated animal re-identification from video. Multiple Object Tracking alone does not provide a sufficiently good re-identification, hence we propose to augment the process by further clustering the output tracks. Unlike datasets for person and vehicle identification, existing animal datasets are not substantial enough to train an advanced model for conventional clustering. In this paper, we present a Classification-Based Clustering method (CBC) which employs track labels and temporal constraints to train a bespoke model for each video dataset. Our proposed method works better than using the tracks alone as animal identities. It also outperforms 13 alternative clustering methods applied to the tracking results.