arXiv AI
Causal neural set filtering for online multi-target tracking
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computer Vision
Sep 30
ReWorld-Track: A Recursive Event World Model for Language-Guided Multi-Camera Tracking
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.
arXiv Computation and Language
Oct 28
Rethinking State Tracking in Recurrent Models Through Error Control Dynamics
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.
arXiv Computer Vision
Sep 1
MotionSync: Non-Causal Refinement of Causal Tracker for Label-Efficient 3D Perception
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...
Hugging Face Trending Papers
Sep 22
Destination Support Restoration for Finite-Set Multimodal Trajectory Prediction
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 Computer Vision
Oct 26
GenTrack2: An Improved Hybrid Approach for Multi-Object Tracking
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...
arXiv Computer Vision
Oct 26
GenTrack3: Hybrid Stochastic-Deterministic Online Multi-Object Tracking with Cluster-Aware Association
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.