The paper introduces GrayTrack, a vehicle‑tracking system that fuses weak, indirect observations from third‑party sensors with sparse direct sensor data using a road‑constrained particle filter. Experiments on a CARLA‑Mininet‑WiFi pipeline show that the system achieves an F1 score of 0.989 for anonymous vehicle passages and reduces trajectory RMSE by 60.1% while cutting catastrophic track loss from 35.8% to 0.3%. These results demonstrate that incorporating indirect third‑party observations can substantially extend tracking capabilities when direct sensor access is limited.
Vehicle tracking is fundamental to applications ranging from urban mobility and public safety to security and defense. Conventional tracking relies on direct access to sensors that provide strong obse...
arXiv:2609.12771v1 Announce Type: cross Abstract: Cross-vehicle federated learning enables vehicles to collaboratively improve perception models while keeping locally collected driving data private....
The paper proposes a label‑free 3D perception framework where roadside units (RSUs) act as unsupervised teachers for self‑driving cars. RSUs learn local 3D detectors from unlabeled data and broadcast predictions to passing vehicles, which use these as pseudo‑labels to train an ego‑centric detector. In a CARLA simulation, the method achieves 82.3% AP for vehicle detection, approaching a fully supervised upper bound of 94.4%, and demonstrates scalability and complementarity with existing ego‑centric approaches.
arXiv:2609.18363v1 Announce Type: new Abstract: Online multi camera 3D tracking must maintain scene global identities across synchronized views, yet query-based trackers carry these identities only i...
arXiv:2608. 16167v1 Announce Type: cross Abstract: High-precision radio map construction is essential for emerging 6G Integrated Sensing and Communication (ISAC) applications, including digital twins and intelligent transportation.
arXiv:2607. 13891v1 Announce Type: new Abstract: Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications.
arXiv:2610.08573v1 Announce Type: new Abstract: Cooperative perception improves autonomous driving by sharing complementary observations among vehicles and roadside infrastructure for 3D object detec...
arXiv:2609.06195v1 Announce Type: new Abstract: This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds...
High-precision radio map construction is essential for emerging 6G Integrated Sensing and Communication (ISAC) applications, including digital twins and intelligent transportation. However, existing deep learning methods predominantly treat this as a pure image completion task, resulting in over-smoothed reconstructions that fundamentally erase high-frequency scattering signatures of dynamic physical entities such as hidden vehicles.
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.
The paper introduces a deployment‑focused evaluation protocol for pedestrian tracking that isolates tracker performance by using shared detections, assessing initialization, gap continuation, identity recovery, close‑neighbor association, and runtime under load, while still reporting HOTA as an aggregate metric. Applied to the JackRabbot Dataset, the protocol reveals that trackers vary widely in capability, with none maintaining spatially correct, same‑identity output after 1 s of missing detector support, and that runtime on an NVIDIA Jetson Orin often falls below the 10 Hz target in crowded scenes. The authors provide open‑source code and scripts to enable reproducible assessment of tracker suitability for real‑world pedestrian‑centric robotics. whyItMatters":"The protocol offers a practical, tracker‑only framework that directly informs the design and deployment of mobile robots operating among pedestrians by highlighting specific failure modes and computational constraints that aggregate scores overlook."
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.