arXiv Computer Vision
GenTrack2: An Improved Hybrid Approach for Multi-Object Tracking
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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.
arXiv AI
Sep 16
Tracking the Unseen: An Occlusion-Robust Framework for Target Tracking Under Full and Long-Term Occlusion
arXiv:2609.17427v1 Announce Type: cross Abstract: Real-time multi-object tracking systems remain highly vulnerable to full and long-term occlusion, where targets temporarily or completely disappear f...
arXiv AI
Jul 21
VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects
arXiv:2607. 17157v1 Announce Type: cross Abstract: Multi-object tracking (MOT) aims to localize multiple objects in videos while preserving their identities over time.
arXiv AI
Jun 24
Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking
arXiv:2606. 23604v2 Announce Type: replace-cross Abstract: The tracking-by-detection paradigm in multi-object tracking (MOT) typically relies on static appearance descriptors to complement motion estimation.
arXiv AI
Jun 30
SUMO: Segment and Track Any Motion with Nonlinear State Space Models
arXiv:2606. 29861v1 Announce Type: cross Abstract: Visual Object Tracking (VOT) and Moving Object Segmentation (MOS) are two fundamental tasks in computer vision that involve both spatial and temporal object dynamics.
arXiv Computer Vision
Sep 22
Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT
This paper conducts a systematic empirical study of multi‑object tracking (MOT) algorithms, focusing on how detection and association components affect overall performance. By evaluating state‑of‑the‑art methods on benchmarks such as MOT16/17/20, SportsMOT, DanceTrack, and CrowdTrack, the authors find that detection quality has a far greater impact than association strategies, and that transformer‑based end‑to‑end models are more robust to detection variations but computationally expensive. The study provides a unified pipeline diagram and practical guidance for researchers and practitioners in selecting and designing MOT systems.