[
    {
        "id": "osp-24037",
        "type": "article-journal",
        "title": "FFBL-Coop: Association-Decoupled Cooperative 3D Multi-Object Tracking",
        "author": [
            {
                "family": "Wu",
                "given": "Haoxin"
            },
            {
                "family": "Bai",
                "given": "Xiaokai"
            }
        ],
        "URL": "https://omanscience.com/en/articles/ffbl-coop-association-decoupled-cooperative-3d-multi-object-tracking",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Cooperative 3D tracking must integrate complementary observations across agents and time while maintaining consistent identities. When evidence integration and identity inheritance share a matching decision, errors arising from cross-view appearance differences and spatial misalignment can compromise both feature fusion and track continuity. We propose FFBL-Coop, a fuse first, bind later framework that separates instance admission from identity management. Confidence-ranked Slot Admission (CSA) allocates cooperative queries to available ego slots using confidence and spatial proximity. Unified Representation Aggregation (URA) uses cooperative semantic features and aligned anchors to guide ego-feature retrieval, refining the augmented query bank within a shared transformer decoder. After refinement, Cooperative-Priority Identity Anchoring (CPIA) combines learned association with persistent mappings to establish accepted identity assignments across frames. A shared codebook reduces transmitted payload while retaining AP and AMOTA close to the uncompressed variant. FFBL-Coop achieves AMOTA/AP of 0.611/0.548 on V2X-Seq and 0.688/0.653 on Griffin-25M. Code will be released."
    }
]