ARGUSTRACK：一种用于多目标跟踪的多视图标注系统

Computer Science > Computer Vision and Pattern Recognition • *arXiv:2606.20687** (cs) [Submitted on 14 Jun 2026] Title:ARGUSTRACK: A Multi-View Annotation System for Multi-Object Tracking Authors: Hao Vo (https://arxiv.org/search/cs?searchtype=author&query=Vo,+H), Duc Nguyen (https://arxiv.org/search/cs?searchtype=author&query=Nguyen,+D), Ngan Le (https://arxiv.org/search/cs?searchtype=author&query=Le,+N) View a PDF of the paper titled ARGUSTRACK: A Multi-View Annotation System for Multi-Object Tracking, by Hao Vo and 2 other authors View PDF (https://arxiv.org/pdf/2606.20687) HTML (experimental) (https://arxiv.org/html/2606.20687v1) Abstract:Multi-Camera Multi-Target (MCMT) tracking has emerged as a critical capability for applications ranging from autonomous driving to animal behavior monitoring. While recent advances have yielded sophisticated tracking algorithms, the availability of annotated multi-view data remains a significant bottleneck. Existing annotation tools predominantly support single-camera workflows or rely on LiDAR sensors, making cross-view labeling tedious and impractical for camera-only setups. We present ARGUS-TRACK, a multi-camera annotation system that addresses these limitations by enabling annotators to work directly on a bird's-eye-view (BEV) plane. Given calibrated camera parameters, a single ground-plane annotation is automatically projected into 2D bounding boxes across all relevant views, inherently ensuring identity consistency without manual cross-view alignment. To further accelerate the labeling process, ARGUSTRACK incorporates two complementary mechanisms: a Temporal Aware module that propagates annotations from preceding frames to initialize new ones, requiring only minor positional adjustments; and a Multi-camera Semi-annotation module that leverages off-the-shelf 2D detectors combined with foot-point estimation to automatically generate candidate BEV positions for annotator verification. We evaluate ARGUSTRACK through a pilot study on multi-camera broiler tracking and demonstrate that it substantially reduces annotation time compared to conventional single-camera labeling workflows. Focus to learn more arXiv-issued DOI via DataCite (pending registration) | Submission history From: Duc Nguyen [view email (https://arxiv.org/show-email/547c173c/2606.20687)] • *[v1]** Sun, 14 Jun 2026 19:18:23 UTC (3,894 KB) Full-text links: Access Paper: View a PDF of the paper titled ARGUSTRACK: A Multi-View Annotation System for Multi-Object Tracking, by Hao Vo and 2 other authors • View PDF (https://arxiv.org/pdf/2606.20687) • HTML (experimental) (https://arxiv.org/html/2606.20687v1) • TeX Source (https://arxiv.org/src/2606.20687) view license (http://arxiv.org/licenses/nonexclusive-distrib/1.0/) Current browse context: cs.CV < prev (https://arxiv.org/prevnext?id=2606.20687&function=prev&context=cs.CV) \| next > (https://arxiv.org/prevnext?id=2606.20687&function=next&context=cs.CV) new (https://arxiv.org/list/cs.CV/new) \| recent (https://arxiv.org/list/cs.CV/recent) \| 2026-06 (https://arxiv.org/list/cs.CV/2026-06) Change to browse by: cs (https://arxiv.org/abs/2606.20687?context=cs) References & Citations • NASA ADS (https://ui.adsabs.harvard.edu/abs/arXiv:2606.20687) • Google Scholar (https://scholar.google.com/scholar_lookup?arxiv_id=2606.20687) • Semantic Scholar (https://api.semanticscholar.org/arXiv:2606.20687) export BibTeX citation Bookmark   Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? 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