← Back to all news
arXiv Computer Vision By Taigo Sakai, Kazuhiro Hotta, Hiroki Kouno, Naoki Kato

GRACE: Geometry- and Ray-Aware Camera-Efficient Multi-View Pedestrian Tracking

Read the original on arXiv Computer Vision →

GRACE is a camera‑efficient multi‑view pedestrian tracker that reduces the number of required cameras while maintaining high tracking accuracy. It combines volumetric‑guided fusion of homography‑based BEV features with 3D‑lifted features, uses ray conditioning to incorporate each camera’s viewing direction, and employs BEV Track Recovery to continue existing tracks with low‑confidence detections. On the WildTrack dataset, GRACE raises MOTA from 83.54 to 91.07 compared to the baseline TrackTacular.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
Aug 11

ATLASFusion: Aggregation Tracking with Location-Aware Sparse Fusion for Robust Spatio-Temporal Multi-View Pedestrian Tracking

arXiv:2509. 08421v2 Announce Type: replace-cross Abstract: For multimedia spatial intelligence through time, multi-view multi-object tracking (MVMOT) suffers from persistent challenges in maintaining consistent object identities across different camera views, leading to tracking inaccuracies.

By Keisuke Toida, Taigo Sakai, Takeshi Nakamura, Hiroshi Shimizu, Kazuhiro Hotta
benchmarks
More like this →
arXiv Machine Learning
Oct 15

The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking

The paper investigates how detector architecture, model scale, scene density, and inference regime influence multi‑pedestrian tracking performance. It compares YOLO11, RT‑DETR, and YOLO26 detectors with ByteTrack and BoT‑SORT trackers on MOT17, MOT20, and SOMPT22 datasets, finding RT‑DETR best on HOTA but at higher cost, while YOLO26‑L with NMS balances quality and speed. End‑to‑end inference for YOLO26 did not consistently improve tracking over NMS.

By Pedro Manoel Herminio Alves, Dalton C\'ezane Gomes Valadares, Cl\'audio de Souza Baptista
benchmarks
More like this →