MCBLT: Multi-Camera Multi-Object 3D Tracking in Long Videos
Journal:
arXiv
Published Date:
Dec 1, 2024
Abstract
Object perception from multi-view cameras is crucial for intelligent systems,
particularly in indoor environments, e.g., warehouses, retail stores, and
hospitals. Most traditional multi-target multi-camera (MTMC) detection and
tracking methods rely on 2D object detection, single-view multi-object tracking
(MOT), and cross-view re-identification (ReID) techniques, without properly
handling important 3D information by multi-view image aggregation. In this
paper, we propose a 3D object detection and tracking framework, named MCBLT,
which first aggregates multi-view images with necessary camera calibration
parameters to obtain 3D object detections in bird's-eye view (BEV). Then, we
introduce hierarchical graph neural networks (GNNs) to track these 3D
detections in BEV for MTMC tracking results. Unlike existing methods, MCBLT has
impressive generalizability across different scenes and diverse camera
settings, with exceptional capability for long-term association handling. As a
result, our proposed MCBLT establishes a new state-of-the-art on the AICity'24
dataset with $81.22$ HOTA, and on the WildTrack dataset with $95.6$ IDF1.
