3 papers
cs.CV2026
CAT-Free: Multi-View Pedestrian Localization without Calibration, Annotations, or Target-Scene Training via Adaptive Geometric Filtering
Taigo Sakai, Hiroki Kouno, Naoki Kato +1
Multi-camera pedestrian localization is useful for wide-area monitoring in public and commercial spaces. However, deploying these systems often requires considerable setup for each…
cs.CV2026
GRACE: Geometry- and Ray-Aware Camera-Efficient Multi-View Pedestrian Tracking
Taigo Sakai, Kazuhiro Hotta, Hiroki Kouno +1
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…
cs.CV2024
Gr-IoU: Ground-Intersection over Union for Robust Multi-Object Tracking with 3D Geometric Constraints
Keisuke Toida, Naoki Kato, Osamu Segawa +2
We propose a Ground IoU (Gr-IoU) to address the data association problem in multi-object tracking. When tracking objects detected by a camera, it often occurs that the same object…