arXiv (All)
open access
CDKF-Track: Cluster-aware Data-Driven Kalman Filtering for Cooperative 3D Multi-Object Tracking
Multi-Object Tracking (MOT) is essential for EdgeAI perception systems, where accurate object localization and reliable identification enable safe decision-making. Singleagent MOT suffers from occlusions, sensor noise, and partial scene understanding in complex real-world scenarios. While multi-agen
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- GOSPA and T-GOSPA quasi-metrics for evaluation of multi-object tracking algorithms#163172
- Beyond the Image Plane: World-Grounded Queries for Multi-Object Tracking#820921
- UGO: Unified Architecture for General Multi-Object Tracking by Segmentation#1141662
- Tracking-by-detection in Multi-object Tracking: Survey and Experiments#985636
- Deep learning architectures for autonomous perception: from sensor auto-calibration to efficient multi-object tracking#673256
Record · ID 1061304
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive