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GenTrack3: Hybrid Stochastic-Deterministic Online Multi-Object Tracking with Cluster-Aware Association — arXiv:2608.09581

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.09581
License (as archived): CC0-1.0
Archived: 2026-10-19T09:13:09.635Z
SHA-256 of the archived text: 0d2003b51afc5a7c26d501f9af94cb9895cee983943f7188fde890ce5863fbe4
Stable URL of this page: https://metraipolis.com/documento/dc_476edcc271ae

How to cite

GenTrack3: Hybrid Stochastic-Deterministic Online Multi-Object Tracking with Cluster-Aware Association — arXiv:2608.09581 — arXiv (en), https://arxiv.org/abs/2608.09581 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_476edcc271ae (Metraipolis archive dc_476edcc271ae, sha256 0d2003b51afc5a7c26d501f9af94cb9895cee983943f7188fde890ce5863fbe4)

BibTeX

@misc{dc476edcc271ae,
  title = {GenTrack3: Hybrid Stochastic-Deterministic Online Multi-Object Tracking with Cluster-Aware Association — arXiv:2608.09581},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.09581},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_476edcc271ae; sha256 0d2003b51afc5a7c26d501f9af94cb9895cee983943f7188fde890ce5863fbe4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.09581. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multi-object tracking (MOT) involves maintaining consistent target identities as objects dynamically enter and leave a scene. Deterministic approaches, such as tracking-by-detection with data association, produce reproducible results and are computationally efficient, but they rely heavily on motion models and are sensitive to noisy detections that can lead to association errors. In contrast, stochastic methods explicitly model uncertainty and can better handle complex non-linear dynamics, albeit at the cost of increased computational complexity and variability arising from random sampling. This paper presents an online MOT framework that integrates deterministic and stochastic principles to achieve robust tracking under uncertainty. Furthermore, a novel track-to-detection matching approach is introduced to enhance scalability with increasing target numbers while supporting group tracking. The tracking inference mechanism employs a tracklet that includes identifiers, states, velocities, track penalties and track ages of targets, supporting a systematic tracking pipeline. Each target is associated with a stochastic particle set to compute the matching cost to detections. Reference implementations of the proposed approach and baseline trackers can be found on GitHub: this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-11 in cs.RO).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2608.09581 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T09:11:58.832Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).