Unsupervised Discovery of Long-Term Spatiotemporal Periodic Workflows in Human Activities

• URL: http://arxiv.org/abs/2511.14945v2
• Date: Thu, 20 Nov 2025 16:11:12 GMT
• Title: Unsupervised Discovery of Long-Term Spatiotemporal Periodic Workflows in Human Activities
• Abstract summary: We introduce the first benchmark comprising 580 multimodal human activity sequences.<n>The benchmark supports three evaluation tasks aligned with real-world applications.<n>We also propose a lightweight, training-free baseline for modeling diverse periodic workflow patterns.
• Score: 52.672726750323996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Periodic human activities with implicit workflows are common in manufacturing, sports, and daily life. While short-term periodic activities -- characterized by simple structures and high-contrast patterns -- have been widely studied, long-term periodic workflows with low-contrast patterns remain largely underexplored. To bridge this gap, we introduce the first benchmark comprising 580 multimodal human activity sequences featuring long-term periodic workflows. The benchmark supports three evaluation tasks aligned with real-world applications: unsupervised periodic workflow detection, task completion tracking, and procedural anomaly detection. We also propose a lightweight, training-free baseline for modeling diverse periodic workflow patterns. Experiments show that: (i) our benchmark presents significant challenges to both unsupervised periodic detection methods and zero-shot approaches based on powerful large language models (LLMs); (ii) our baseline outperforms competing methods by a substantial margin in all evaluation tasks; and (iii) in real-world applications, our baseline demonstrates deployment advantages on par with traditional supervised workflow detection approaches, eliminating the need for annotation and retraining. Our project page is https://sites.google.com/view/periodicworkflow.

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