ARGUSTRACK: A Multi-View Annotation System for Multi-Object Tracking

• URL: http://arxiv.org/abs/2606.20687v1
• Date: Sun, 14 Jun 2026 19:18:23 GMT
• Title: ARGUSTRACK: A Multi-View Annotation System for Multi-Object Tracking
• Abstract summary: Multi-Camera Multi-Target (MCMT) tracking has emerged as a critical capability for applications ranging from autonomous driving to animal behavior monitoring.<n>Existing annotation tools predominantly support single-camera or rely on LiDAR sensors, making cross-view labeling tedious and impractical for camera-only setups.<n>We present ARGUS-TRACK, a multi-camera annotation system that enables annotators to work directly on a bird's-eye-view plane.
• Score: 18.084425792911578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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