ReWorld-Track introduces a recursive event world model for language‑guided multi‑camera tracking that explicitly carries association uncertainty into future predictions. By treating candidate matches and waiting as alternative target states, the model updates a persistent recurrent belief that preserves uncertainty across successive observations. This approach improves identity continuity and next‑camera accuracy, achieving HOTA scores of 65.19 on CityFlowV2 and 45.36 on MTMMC, and reducing median arrival‑time error from 0.78 s to 0.71 s.
By Haoyang Wu, Shoudong Han, Chaoyue Li, Sijia Chen, Zhenyang Xie, Wang sihan
The paper argues that error control is as crucial as expressive capacity for state tracking in recurrent models. It proves that affine recurrent networks, including State‑Space Models and Linear Attention, cannot correct errors along state‑separating subspaces once they preserve state representations, leading to finite‑horizon solutions governed by accumulated error. Empirical results on group state‑tracking tasks show that tracking collapses when the distinguishability ratio falls below a readability threshold, and this crossing predicts downstream accuracy failure.
By Jiwan Chung, Heechan Choi, Seon Joo Kim
arXiv:2608.29567v1 Announce Type: new
Abstract: Three-dimensional box-and-track annotation is the cost bottleneck in autonomous-driving data engines, and the offline systems built to relieve it repla...
By Rahul Ahuja, Bala Murali Manoghar Sai Sudhakar, Shashwata Gupta, Venkatraman Narayanan, Varun Ravi Kumar, Senthil Yogamani
Robots operating around pedestrians often reason over a finite set of predicted human futures. Repeated online updates can concentrate this limited prediction budget on dominant destinations and leave...
arXiv:2510.24410v5 Announce Type: replace
Abstract: This paper proposes a visual multi-object tracking method that jointly employs stochastic and deterministic mechanisms to ensure identifier consist...
By Toan Van Nguyen, Rasmus G. K. Christiansen, Dirk Kraft, Leon Bodenhagen
GenTrack3 is an online multi-object tracking framework that blends deterministic tracking-by-detection with stochastic particle-based uncertainty modeling. It introduces a cluster-aware track-to-detection matching strategy to improve scalability and support group tracking, while maintaining a systematic pipeline that tracks identifiers, states, velocities, penalties, and ages. The authors provide reference implementations on GitHub for reproducibility.
By Toan Van Nguyen, Rasmus G. K. Christiansen, Dirk Kraft, Leon Bodenhagen
CST‑WM is a causally structured world model designed for embodied visual tracking, where a robot must keep a moving target visible and recover it after occlusion or drift. The model separates state into target‑evidence, robot, and observation branches, removing direct action‑to‑target‑evidence edges to prevent causal hallucination and instead letting actions influence evidence through robot motion and resulting views. Evaluated on EVT‑Bench, Habitat 3.0, and real‑world trials with a Unitree Go2 quadruped, CST‑WM outperforms reactive trackers and other world‑model baselines in following, distance control, safety, and re‑acquisition, achieving 20 of 30 successful real‑world recoveries versus 14 for TrackVLA.
By Junyi Hu, Shuaihang Yuan, Jiazhao Liang, Yi Fang
Training query-propagation end-to-end multi-object tracking (MOT) models requires dense bounding-box and identity annotations across video sequences, making dataset construction expensive. Clip-level active learning reduces this cost by selecting video clips for annotation, but prior acquisition criteria based on output-level temporal uncertainty may miss clips whose informativeness comes from association instability in propagated track states.
End-to-end multi-object trackers have narrowed the gap with classical tracking-by-detection on association-difficult benchmarks. Yet they still make spatially implausible errors no classical tracker w...
arXiv:2606. 07254v1 Announce Type: new Abstract: State tracking exposes a sharp limitation of sequence models: the relevant signal is often not a summary of observed tokens, but an ordered latent state that evolves through non-commutative transformations.
By Jeonghoon Lee
arXiv:2610.10391v1 Announce Type: new
Abstract: End-to-end multi-object trackers have narrowed the gap with classical tracking-by-detection on association-difficult benchmarks. Yet they still make sp...
By Beno\^it Roussel, Damien Bouet, Liming Chen, Pierre Perrault
arXiv:2407.04308v4 Announce Type: replace-cross
Abstract: We propose a graph-based tracking formulation for multi-object tracking (MOT) where target detections contain kinematic information and re-id...
By Griffin Golias, Masa Nakura-Fan, Vitaly Ablavsky