Multi-view pedestrian tracking improves with fewer cameras
GRACE: Geometry- and Ray-Aware Camera-Efficient Multi-View Pedestrian Tracking
Computer Vision and Pattern Recognition
Summary
Tracking people using cameras is easier with many cameras, but using fewer cameras cuts costs. The authors introduce GRACE, a method that helps track pedestrians accurately even with just two cameras. It combines different types of 3D and bird’s-eye view features and conditions on each camera’s direction to improve tracking. Their method reduces errors and keeps track of people better than earlier approaches.
What this means in practice
- •For security system designers: Build pedestrian tracking systems that maintain accuracy while using fewer cameras for cost-effective surveillance monitoring.
- •For smart city infrastructure teams: Deploy pedestrian tracking that reduces hardware costs without sacrificing tracking quality in urban environments.
Authors
Taigo Sakai, Kazuhiro Hotta, Hiroki Kouno, Naoki Kato
Abstract
Reducing the number of cameras reduces the deployment cost but removes views that correct BEV responses stretched away from true pedestrian positions by projection and short score drops that can split tracks} in Bird's-Eye View (BEV) tracking. We introduce GRACE, a camera-efficient multi-view tracker with three components. Volumetric-Guided Fusion combines homography-based BEV features with features lifted through 3D space. Ray Conditioning exposes each camera's viewing direction to the fusion network. Its tracking component, BEV Track Recovery (BTR), uses low-confidence detections only to continue existing tracks. The same detections cannot start new tracks. With two WildTrack cameras, GRACE improves MOTA from 83.54 for TrackTacular, our baseline, to 91.07.