[Submitted on 18 Sep 2026]
Title:Hand-Aware Transition Modeling for Bimanual Procedural Anomaly Detection
View a PDF of the paper titled Hand-Aware Transition Modeling for Bimanual Procedural Anomaly Detection, by Di Wen and 8 other authors
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Abstract:Procedural anomaly detection in bimanual assembly requires judging each hand action against the execution so far. A corrective action may look unusual in isolation, while a visually plausible action can violate the order of the procedure. We present HACT, a transition model over predicted per-hand events. A role-preserving history keeps the concurrent responsibilities of both hands, and a marked temporal point process assigns each observed transition a semantic and temporal surprisal. A supervised evidence head and a two-state filter convert these surprisals into per-hand anomaly posteriors. A recovery-aware protocol on predicted events and participant-disjoint folds reports the recovery false-positive rate at an operating point selected on validation participants. On two bimanual power-tool procedures HACT has the highest AUPRC and F1 among the compared methods and the fewest recovery alarms. Applied without retraining to a different assembly order of the same product, it retains the highest AUPRC and F1. The source code is available at this https URL.
Comments: 6 pages, 1 figure, 3 tables. Code: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.21207 [cs.CV]
(or arXiv:2609.21207v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.21207
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Di Wen [view email] [v1] Fri, 18 Sep 2026 01:44:36 UTC (2,096 KB)
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