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

2026-06-14

AI Summary

This work addresses the lack of efficient and identity-consistent annotation tools for purely vision-based multi-camera multi-object tracking, where prevailing approaches relying on single-view imagery or LiDAR often fail to ensure cross-view identity coherence. To overcome this limitation, we propose the first unified bird’s-eye-view (BEV)-based annotation framework that enables one-time labeling in the BEV plane and automatically projects these annotations into 2D bounding boxes across all camera views. The system integrates camera calibration, off-the-shelf 2D detectors, and footpoint estimation, and further accelerates the annotation process through temporal propagation and semi-automatic candidate generation. Evaluated on a multi-camera poultry tracking task, our method substantially improves annotation efficiency, significantly reducing manual effort compared to conventional per-camera labeling while rigorously preserving cross-view identity consistency.

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.

Problem

• Multi-Camera Multi-Target Tracking
• Annotation Bottleneck
• Cross-View Labeling
• Camera-Only Setup
• Identity Consistency

Innovation

• multi-camera multi-target tracking
• bird's-eye-view annotation
• temporal propagation
• semi-automatic labeling
• cross-view consistency