GRACE: Geometry- and Ray-Aware Camera-Efficient Multi-View Pedestrian Tracking
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 15 Sep 2026.
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References · 12
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 823
- 2016Performance Measures and a Data Set for Multi-Target, Multi-Camera Trackingcited by 692
- 2022PETR: Position Embedding Transformation for Multi-View 3D Object Detectioncited by 552
- 2020Tracking Objects as Pointscited by 483
- 2020Multiview Detection with Feature Perspective Transformationcited by 71
- 2023CAPE: Camera View Position Embedding for Multi-View 3D Object Detectioncited by 40
- 2023EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye Viewcited by 33
- 2024Lifting Multi-View Detection and Tracking to the Bird's Eye Viewcited by 25
- 2023Viewpoint Equivariance for Multi-View 3D Object Detectioncited by 18
- 2022Detection Recovery in Online Multi-Object Tracking with Sparse Graph Trackercited by 15
- 2024RayFormer: Improving Query-Based Multi-Camera 3D Object Detection via Ray-Centric Strategiescited by 9
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